GIS-based Planning Support System for Transportation
and Industrial Location Analyses:
A Case Study of the Cokemaking Sector in Shanxi Province, China
by
Yan Chen
Bachelor of Architecture, 1998
Tsinghua University, China
Submitted to the Department of Urban Studies and Planning and the Center for
Real Estate in partial fulfillment of the requirements for the degrees of
Master in City Planning and
of
Master Science in Real Estate Development
at the
MASSACHUSETTS INSTITUTE OF TECHNOLOGY
February 2003
@2003 Yan Chen. All Rights Reserved
ROTCH
MASSAC HUSTTSNSTITUTE
TECHNOLOGY
OF
MA 1 9 2003
LIEBRARIES
The author hereby grants to MIT permission to reproduce and to distribute
publicly paper and electronic copies of this thesis document in whole or in part.
Author
Certified by
Accepted b
Accepted b
Department oft rban Studies and Planning
Center for Real Estate
January, 2003
Professor Karen R. Polenske
Professor of Regional Political Economy and Planning
Thesis Supervisor
Professor Dennis Frenchman
Chair, MCP Committee
Studies and Planning
Urban
of
Department
Prdfessor William C. Wheaton
Chairman, Interdepartmental Degree in Real Estate Development
GIS-based Planning Support System for Transportation
and Industrial Location Analyses:
A Case Study of the Cokemaking Sector in Shanxi Province, China
by
Yan Chen
Submitted to the Department of Urban Studies and Planning and the Center for
Real Estate in partial fulfillment of the requirements for the degrees of Master in
City Planning and Master of Science in Real Estate Development.
ABSTRACT
I created a Shanxi Province GIS -based Planning Support System (SPGPSS) for
transportation and industrial plant location studies of the cokemaking sector in
Shanxi Province.
By integrating database, map viewer, scripts, and professional models in the GIS
environment, on the provincial level, I designed the SPGPSS to have capabilities
of optimizing plant locations, transport routes and modes under the different
scenarios and computing the corresponding cost, energy consumption, and
pollution emissions in the transportation process. Policy makers and industrial
organizations can utilize the SPGPSS to value the economic and environmental
impacts from different policy possibilities and assist their planning decisions on
location rearrangements and structural changes. On the plant level, a plant
manager can use the SPGPSS to conduct spatial analyses and multi-plan
valuations for an individual plant in the planning of transport routes and new plant
location.
By the applications of SPGPSS, I tested my hypothesis that combining
cokemaking plants into several large-capacity plants or industrial parks is
preferable to having them distributed throughout the area. From the perspective
of total cost, the large-capacity plants and industrial parks instead of the
distributed small-capacity plants would reduce the total cost both from the
transportation and cokemaking process. From the perspective of total energy
consumption and pollution emissions, however, the large-capacity plants and
industrial parks would increase the total energy consumption and pollution
emissions. Thus, my hypothesis is only partially proven.
'
Thesis Supervisor: Karen R. Polenske
Title: Professor of Regional Political Economy and Planning
Thesis Reader: David M. Geltner
Title: Professor of Real Estate Finance
ACKNOWLEDGMENTS
I would like to thank my research and thesis supervisor, Professor Karen R.
Polenske, for her continual help, support, and encouragement through out the
entire time I have worked with her. Her kindness and devotedness will always
remain in my heart. This thesis would not have gone anywhere without her
strong support and insightful comments. I also thank Professor David Geltner for
reviewing my thesis and giving me valuable comments and suggestions.
My most sincere appreciation is extended to Professor Steven Kraines and Mr.
Takeyoshi Akatsuka inTokyo University. They started their pioneering
transportation studies in Shanxi Province and gave me lots of enlightenment and
suggestions for my work in this area. I also thank all other team members in the
AGS MRP Group, especially Professor Fang Jinghua from Taiyuan University of
Technology for helping me on the coke-oven technologies.
This research was sponsored by AGS grants (No. 005151-042 and 008282-008)
and NSF grant (No. 006487-001). I thank AGS and NSF for making this
research possible.
Finally I want to thank my parents and my husband for their love and support all
the time.
TABLE OF CONTENTS
T IT L E .... . ...............................................................................
.. .
1
A B S T R A CT .......................................................................................
2
A C K O W LED G E M ENT ........................................................................
3
TA B LE O F C O NTENTS ......................................................................
4
LIST O F FIG U R ES .............................................................................
6
LIST O F TA B LE S ..............................................................................
8
A B B R EV IA T IO NS ..............................................................................
10
IN TR OD U CTIO N ..........................................................................
11
I
2 HYPOTHESIS and OBJECTIVE . .......................................
18
2.1 Hypothesis ....................................................
18
2.2 Objectives........................................................
21
3 LITERATURE REVIEW ....................................................................
23
3.1 GIS-based Planning Support Systems...................
.............
23
3.2 Transportation NETFLOW Model --------------------............................
3.3 Process Flow Model.........................................
33
35
3.4 Industrial Location Theories.......
36
........................
4 METHODOLOGY and PROJECT DESIGN .........................-
4.1 SPGPSS Components and Organization.........
................
39
39
4.1.1 SPGPSS Components..............................
40
4.1.2 SPGPSS Advantages..............................
...........
4.1.3 SPGPSS Organization.....................................
47
49
4.2 Analysis of Alternatives
..................................
4.3 Case Study............................................................
50
54
5 IM PLEM ENTATION .....................................................................
55
5.1 Data Collection and Database Creation.......................................
55
5.2 GIS Modeling, Programming and Processing................................
61
6 APPLICATIONS..........................................................................
64
6.1 Analysis of Alternatives at Provincial Level..................................
64
6.1.1 Minimize Total Cost.........................................................
65
6.1.2 Minimize Total Pollution Emissions and Energy Consumption
72
6.1.3 Coke Oven Technology Impact Analysis............................
74
6.1.4 Highway Construction & Speed Improvement Impact Analysis
6.1.5 Industrial-Park Location-Choice Analysis.............................
76
78
6.2 Plant Case Studies.................................................................
79
6.2.1 Choose transport routes and modes....................................
79
6.2.2 Selection of a new location...............................................
81
6.2.3 Choose coke-oven technology...........................................
83
7 CONCLUSIONSS .........................................................................
84
APPENDICES .................................................................................
86
Appendix A: Transportation Cost, Energy Consumption, and NOx
Emissions for Each Transportation Link inthe GIS System.......................
86
Appendix B: Application Results.........................................................
88
BIBLIOGRAPHY.............................................................................
95
LIST OF FIGURES
Figure 1.1: Country-share of World Coke Production in 2000..................
12
Figure 1.2: Location of Shanxi Province in China...................................
13
Figure 1.3: Cokemaking Supply Chain in Shanxi Province.......................
15
Figure 2.1: Taiyuan Yingxian Non-recovery Coke Ovens.........................
20
Figure 2.2: Qingxu Meijing Large-machinery Coke Ovens.......................
20
Figure 3.1: GIS-based Urban Transportation Planning System in Portland
Metro, Oregon................................................................
25
Figure 3.2: Inter-Oceanic Corridor Passing Through the Republic of Bolivia
26
Figure 3.3: The A IDA IRSystem .........................................................
28
Figure 3.4: NOx Industrial Emissions Plumes on a Fresh Northeasterly
W indy Day in Geneva...................................................
29
Figure 3.5: NOx Emissions Due to Traffic in Geneva Region....................
30
Figure 3.6: Different Land-Rent Gradients............................................
36
Figure 4.1: Structure of GIS-based Planning Support System (GPSS).......
40
Figure 4.2: View of Coal Transportation in Shanxi Province, China............
43
Figure 4.3: View of Coke Transportation in Shanxi Province, China...........
43
Figure 4.4: ArcView's Automatic Links between Map and Database..........
44
Figure 4.5: Comparison between the SPGPSS and System Developed by
Kraines and Akatsuka (K&A System....................................
47
Figure 4.6: System-Flow Chart of GPSS...........................................
49
Figure 6.1: Total Cost of Non-recovery Coke-Oven Technology...............
66
Figure 6.2: Total Cost of Large-machinery Coke-oven Technology............
67
Figure 6.3: Plant-Cost Composition of Three Coke-oven Technology
Optio ns .......................................................................-Figure 6.4: Coke Transportation Choices of X Cokemaking Company by
Railway and by Highway..................................................
Figure 6.5: Locational Choice of X Cokemaking Plant.............................
Figures in the Appendices:
Figure B.1: 2000 Plant-Min Scenario for Total Cost Minimization..............
91
Figure B.2: 2000 Transport-Min Scenario for Total PM Emission
Minim ization..................................................................
92
Figure B.3: Highway System in Shanxi Province....................................
93
Figure B.4: Different Scenarios of Industrial-Park Locations in the PM
Emission Minimization.....................................................
94
LIST OF TABLES
Table 5.1: Coal Production, Coke Production Capacity, and Coke
Consumption in Shanxi Province, 1990 and 2000..................
57
Table 6.1: Total Cost of Non-recovery Coke-Oven Technology...............
66
Table 6.2: Total Cost of Large-machinery Coke-oven Technology............
67
Table 6.3: Plant-Cost Composition of Three Coke-oven Technology
Options........................................................................
74
Table 6.4: Comparisons Before and After the New Highway Construction
and Road-speed Improvements.......................................
77
Tables in the Appendices:
Table A. 1: Transportation-cost Coefficients for Diesel
T rucks..........................................................................
.
86
Table A.2: Transportation Cost Coefficients for Diesel and Electric
Trains.................................................................
86
Table A.3: Transportation Energy Consumption Coefficients for Diesel
Trucks and Trains..........................................................
87
Table A.4: Transportation Energy Consumption Coefficients of Electric
Trains......................................................
87
Table A.5: Transportation NOx Emission Coefficients for Diesel trucks,
Diesel Trains and Electric Trains......................................
87
Table B.1: Road Transportation vs. Rail Transportation (2000 Base
Scenario)...........................................
88
Table B.2: Coal Transportation vs. Coke Transportation (2000 Base
Scenarios)..................................................
88
Table B.3: Particulate Emissions from Transportation and Cokemaking
Plants....................................................... ......
88
Table B.4: SOx Emissions from Transportation and Cokemaking Plants,
2000............................................
89
Table B.5: Transportation Energy Consumption, 2000............................
89
Table B.6: Comparison of Three Cokemaking Industrial Park
Scenarios.......................................................................
89
Table B.7: Coke Transportation of X Cokemaking
Pla nt.............................................................................
89
Table B.8: Locational Choice of X Cokemaking Plant: Old Location
Scenario.......................................................................
90
Table B.9: Locational Choice of X Cokemaking Plant: New Location
Scenario........................................................................
90
Table B.10: Plant-Cost Comparison of Different Coke-Oven Technologies..
90
ABBREVIATIONS
AGS
Alliance Global Sustainability
EPB
Environmental Protection Bureau
ESRI
Environmental Systems Research Institute
GIS
Geographic Information System
GPS
Geo-referenced Positioning System
GPSS
GIS-based Planning Support System
GUI
Graphic User Interface
IOPM
Input-Output Process Model
MRP
Multiregional Planning
NOx
Nitrogen Oxide(s)
Plant-Min
Plant-Minimization
PM
Particulate Matter
RMB
Renminbi (Chinese Currency)
SOEs
State-Owned Enterprises
SOx
Sulfur Oxide(s)
SPGPSS
Shanxi Province GIS -based Planning Support System
Transport-Min
Transport-Minimization
TVEs
Town and Village Enterprises
Chapter 1
INTRODUCTION
With the advances in information technology and database management,
Geographic Information System (GIS) technology has been widely used by
analysts for planning decision-making. GIS makes it possible to store and
visualize graphic maps together with linked attribute data. Planners can use GIS
as a database management system to perform spatial data storing and
quantitative analyses based on visual maps. GIS also provides a platform for
planners to work with other application software and programming languages.
Nowadays, government, academia, and business analysts use GIS for various
applications, including making thematic maps, building their own analyses and
query models, and developing sophisticated planning support systems.
A GIS-based Planning Support System (GPSS), such as the one I
developed in this study, is a computer-based system that uses GIS technologies
to assist decision makers and policy analysts in the planning process by
conducting spatial information processing and studies. Such systems provide
simulations of planning alternatives in a GIS environment and utilize the GIS
spatial tools or other professional models linked with the system to conduct
analyses for specific purposes. Planners and decision makers can refer to these
simulation results and choose the best alternative. Planners have widely used
GPSS in the assessment of public policies and strategies in various areas.
Examples include the Geneva AIDAIR project, a GPSS, to help decision makers
assessing the impact of urban air-quality management in the Geneva region,
Switzerland (CUEH, 1998). The German GAF mbH Company developed and
implemented a GPSS to support multi-model transportation studies, focusing on
the inter-oceanic corridors in Bolivia (GAF mbH Company, 1998). Those
systems successfully simulate the environmental and transportation initiatives
and evaluate the possible impacts from those policy and project implementations.
Based on the GIS technologies, GPSS can play a very useful and crucial role in
the planning decision-making process.
Coke is a very important energy and commercial commodity in the
international trade markets. While the cokemaking production capacities in
Europe and the United States have been dwindling in the past decade, China
has been greatly increasing its production capacity and is becoming the largest
coke production and consumption country in the world. In 2000, China produced
approximately 30 percent of the total coke production in the world (Figure 1.1,
Coke Outlook 2000 Conference), while its coke exports made up approximately
78 percent of the total exports in the world (Hua, 2001).
Figure 1.1: Country-share of World Coke Production in 2000
Country-share of Coke Production in 2000
Others
14%
Poland30
S. Aftc
German
Australia
6%
Russia
6%
U.S.
India
6%
Source: Coke Outlook 2000 Conference
Shanxi Province is located in the north of China (Figure 1.2) and
possesses abundant coal resources. Currently, Shanxi Province produces the
most coke in China, representing about 40 percent of the total coke production in
China and 16 percent of the total coke production in the world (Polenske and
McMichael, 2002). In 2000, Shanxi Province exported 6,440,000 tonnes of coke
to the international coke market (Shanxi Statistical Yearbook, 2001). Shanxi also
supplies a great amount of coke to domestic users, including iron-steel plants,
electric-power plants, chemical plants, etc. Shanxi Province has become the
biggest coke production and export region in the world.
Figure 1.2: Location of Shanxi Province in China
Shanxt Provnce
Source: http://www.chinatour.com/map/a.htm
In the cokemaking sector, the supply chain runs from coalmines to the
coal transportation to cokemaking plants, and then from cokemaking plants to the
coke transportation to coke consumers (Figure 1.3). The cokemaking plant is the
core component of this supply chain, and an analyst can view it as the
connecting point for coal transportation and coke transportation. Coal and coke
transportation play a very important economic and environmental role in the
supply chain of the cokemaking sector in Shanxi Province. The transportation
cost accounts for approximately one-third of the total production cost (AGS MRP
Field Trip Interview, 2001). The choice of transport routes and modes, the
location of the cokemaking plant, and also the access to coalmines and coke
consumers all affect the transportation cost. As we have documented in the
different part of the team research in which I am participating, the cokemaking
industry is a high-pollution and energy-intensive industry (Polenske and
McMichael, 2002). Researchers usually focus on the pollution and energy
consumption from cokemaking plants, but the high pollution and energy
consumption from diesel trucks in the transportation process aroused our
attention on our field trips to Shanxi Province. We realize that the pollution and
energy consumption from coal and coke transportation cannot be ignored. When
we consider the future industrial planning of the cokemaking sector in Shanxi
province, we should measure and minimize the overall cost and pollution both
from plants and the transportation process.
Currently, the Chinese national environmental regulations require closing
the low-efficient and high-pollution small-capacity cokemaking plants and
replacing these plants by some large-capacity plants or enlarging already
existing plants. After the #367 official directive issued by the State Economic and
Trade Commission and State Environmental Protection Agency in 1997, the
Shanxi local government started to close the small-capacity plants using
indigenous and modified indigenous coke ovens and replacing them by the largemachinery cokemaking plants whose annual capacity is usually larger than
200,000 tonnes. In the future provincial industrial planning, the Shanxi
government plans to build several cokemaking industrial parks in the province to
consolidate its cokemaking industry.
Figure 1.3: Cokemaking Supply Chain in Shanxi Province
Cokemaking Supply Chain
Stage I
Coal Mines
"
Coal mining
technology
" Coal quality
Suppliers
Stage 2
Coal Transportation
"""""""
Vehicle
technology
Distributors
Stage 3
Stage 4
Stage 6
Cokemaking Plants Coke Transportation Coke Consumers
iea a a
Oven
Vehicle
technology
technology.
Manufacturers
Distributors
Domesti
Consumers
Information Flow
Source: AGS MRP Team, 2002
Those regulations not only have changed the plant capacity now being
used in Shanxi Province, but also affected the adoption of cokemaking
technologies. The national environmental regulations prohibited the plants from
continuing to use the indigenous and modified-indigenous coke-oven
technologies and recommended that plant managers should adopt the largemachinery coke-oven technology with by-product recovery. Currently, largemachinery coke ovens are the ovens whose height is equal or bigger than 4.3
meters (AGS MRP Team Field Trip Interview, 2002). In China, the machinery
ovens in the range of 2.8-3.2 meters height are called middle-machinery coke
ovens and the ones shorter than 2.5 meters are called small-machinery coke
ovens (Fang, 2002). Inthe United States, all of these ovens are called slot
ovens.
The local Shanxi Environmental Protection Bureau (EPB) also
recommended the non-recovery cokemaking technology, which represents the
least-polluting coke-oven technology and virtually eliminates all hazardous air
pollutants. This technology has better environmental performance than that of
other coking methods, because it operates under negative pressure within the
ovens (Li and Shen, 1995). The Chinese types of non-recovery coke ovens
usually use cold-coal loading and cold-coke unloading, and the smoke released
from these processes is less than the non-recovery coke-oven types used in the
United States (Fang, 2002).
Different coke-oven technologies have different investment costs and land
costs. On the one hand, the use of non-recovery technology has lowered
operational and investment costs but raised land costs due to the intensive land
use. On the other hand, the use of large-machinery coke ovens usually has
raised operational and investment costs but lowered land costs due to its
compact machinery production.
As a result of these policy initiatives, coke managers and policy makers in
Shanxi Province need to integrate factors both from the transportation and the
cokemaking plant (capacity and technology considerations), and optimize the
location and transport routes for large-capacity plants and industrial parks to
reduce the overall costs and pollutants from the cokemaking and transportation
processes. Inthe planning decision-making process, GPSS plays a very
important role. A planner can do simulations under different alternatives and
assist the managers and policy makers to choose the optimized transportation
and industrial plant respective routes and locations. Also, the planner can
develop and use such GPSS systems in the transportation and industriaocation
analyses of other sectors in other regions. The GPSS can generate specific
spatial and quantitative analyses with visual maps for the studied region in the
areas of transportation, location choice, and environmental pollution control.
Chapter 2
HYPOTHESIS and OBJECTIVE
How to create and use the GIS -based planning support system to resolve
the real-world problems is an interesting research topic. I choose the
cokemaking sector in Shanxi Province, China as the study focus and create a
Shanxi Province GIS-based Planning Support System (SPGPSS) to resolve its
transportation planning and industrialocation issues.
2.1 Hypothesis
For the transportation and industrial-location study of the cokemaking
sector in Shanxi Province, my hypothesis is that the large-capacity cokemaking
plants and industrial parks instead of the distributed small-capacity plants will
reduce the total cost, energy consumption, and pollution emissions both from
transportation and the cokemaking process in Shanxi Province, China.
The total cost includes the transportation cost and the plant cost. The
transportation cost is usually one-third of the production cost (AGS MRP Field
Trip Interview, 2001). I consider four kinds of major transportation activities in
the cokemaking sector inShanxi Province: (1)transporting coal by road, (2)
transporting coal by railway, (3)transporting coke by road, (4) transporting coke
by railway. The plant cost consists of three parts. The first part is the operational
cost, which is the cost for the daily operation and maintenance, as measured by
the quantities of all the inputs used in cokemaking and the prices of each input.
It can be separated into materials cost, labor cost, and maintenance cost (Chen,
2000). The second part is the investment cost, which is the cost for the design
and purchase of facilities and equipment. The third part is the land cost, which is
the cost for the purchase of land and the improvement of ground infrastructure.
To estimate the land cost, I use the opportunity cost of land used for farming.
The plant cost is directly related to the plant capacity and the coke-oven
technology. Obviously, the larger the plant capacity, the more the plant cost. To
compare plants of different capacities and technologies, I use the unit plant cost
(total plant cost divided by total production capacity) as the measure. After the
implementation of the 1997 #367 national environmental regulation,' there are
two major recommended coke-oven technologies, large-machinery coke-oven
technology and non-recovery coke-oven technology. The plants using the largemachinery technology usually have a larger capacity than the ones using the
non-recovery technology. From the interviews with coke managers on our field
trips, I found that the large-machinery cokemaking plants usually have higher unit
operational cost and unit investment cost, but lower unit land cost than the nonrecovery cokemaking plants. Because almost all the plants are located in the
countryside and the unit price for the use of land is almost the same in this region,
the amount of land used determines the land cost.2 Because of the
technological requirements, the land use of non-recovery coke ovens is much
more intensive than large-machinery coke ovens with the same capacity (Figures
Refer to the details of the 1997 #367 national environmental regulation in Chapter 1.
The land ownership and property rights in China are very different from those in the United
States, but a detailed explanation is beyond the scope of my current study. Here, I assume that
the plants own the use-right of the land, and the price of the use of land is equal to the
opportunity cost of land used for farming.
2
2.1 and 2.2). That is why the unit land cost of non-recovery cokemaking plants is
much higher than large-machinery cokemaking plants.
Figure 2.1: Taiyuan Yingxian Non-recovery Coke Ovens
Source: Author, 2002 Summer Field Trip in Shanxi Province, China
Figure 2.2: Qingxu Meijing Large-machinery Coke Ovens
Source: Author, 2002 Summer Field Trip in Shanxi Province, China
The location does not affect the land cost much, but it does affect the
transportation cost. The plants close to coalmines, coke consumers, or with easy
access to railways or highways, have lower transportation cost than those farther
away. On the one hand, the scattered small-capacity plants are very locationflexible in that they can locate near suppliers or consumers and choose the best
transportation routes. On the other hand, the large-capacity plants do not have
such advantages. There is therefore an important tradeoff between the
transportation cost and the plant cost. In the case that several distributed small
plants have the same capacity as one big plant, the scattered small plants have
lower total transportation cost due to the contiguity to suppliers and customers
and higher plant cost due to more initial investment. The large-capacity plant
has higher transportation costs because it cannot locate near all its suppliers and
consumers, but it has lower plant costs due to the economies of scale that
accrue as the plant becomes larger.
2.2 Obiectives
By creating a SPGPSS for the transportation and industrial-location choice
study of the selected areas and integrating a transportation model, a plant model,
and a process-flow model and a friendly Graphic User Interface (GUI) within a
GIS environment, I am able to use the SPGPSS to assist real-time decisionmaking.
The following are major issues I explore for coke managers and policy
makers in Shanxi Province:
* What are the locations for specific cokemaking plants and industrial parks that
minimize the total transport cost, energy consumption, or pollution emissions?
* What are the transport routes and modes for specific cokemaking plants and
industrial parks that minimize the total transport cost, energy consumption, or
pollution emissions?
* What are the economic or environmental impacts on the total cost, energy
consumption, and pollution emissions of the plants of using different cokeoven technologies?
* What are the benefits from the location rearrangements and industrialstructure changes as measured by the reduction of the total cost, energy
consumption, or pollution emissions both from the transportation and
cokemaking process?
* What are the benefits from the recent transportation infrastructure
improvements as measured by the reduction of the total cost, energy
consumption, or pollution emissions both from transportation and cokemaking
process?
I am able to test my hypothesis by applying the SPGPSS. The SPGPSS
also provides the plant managers and industrial-park organizations with the
optimized plant locations and transport routes and modes in the province or in
the surveyed region, and shows the benefits from these location rearrangements
and structure changes.
Chapter 3
LITERATURE REVIEW
To create a GIS -based planning support system and conduct
transportation and industrial-location analyses in the case of the cokemaking
industry in Shanxi Province, I did a literature review of the critical aspects of the
GIS-based planning support systems, transportation NETFLOW model, processflow model as well as the industrial-location theories.
3.1 GIS-based Planning Support Systems
The GIS-based Planning Support System (GPSS) has been widely used
in the planning decision-making process in various areas (Gittings et al., 1993).
GPSS has the capability to improve the efficiency and quality of planning and
program development. There are lots of innovative GPSSes using GIS
technologies to solve real-world problems and assist planners and the public
making decisions in the project planning and assessments. Inthe following part,
I review several relevant GPSSes in the areas of transportation and
environmental studies. Those systems are all applied in the real-world planning
process.
* GIS-based Urban Transportation Planning Studies in Portland Metro, Oregon
(U.S. DOT, 1998)
With the region's growing population, Portland Metro needed to reduce
reliance on the car and vehicle miles of travel per capita during the next decade.
It has become an important issue to improve accessibility to employment,
education and non-work activities while traffic congestion is expected to get
worse during the plan period. To analyze these issues, Portland Metro decided
to create a GIS-based urban-transportation planning system and to look at a
range of transportation-system alternatives, including motor-vehicle alternatives
with varying levels of investment in roadway improvements and transit and
pedestrian alternatives with varying levels of investment intransit and pedestrian
access to transit. To evaluate these alternatives, Portland Metro applied a kind
of GPSS with travel-demand forecasting model that predicts how each alternative
would affect transit ridership, traffic congestion, access to jobs, movement of
goods and many other factors. The analyst uses the GPSS to collate and
manage the data needed for the transportation model and also to display the
model outputs, such as predicted employment densities and pedestrian
environment factor. Their GPSS utilizes ArcView GIS as a major GIS
environment.
With the geocoding data of the location of households and activity centers,
they were able to have the GPSS perform accurate spatial analyses of tripgeneration and trip-distribution factors. Compared with the traditional modeling
techniques that may be a poor approximation to reakworld conditions, GIS is
able to measure very accurately the distance or travel time and to produce an
average value for a group of points by using actual network distance from each
site. Thus, the use of geocoded locations in the GPSS improved the accuracy of
the model data and the modeling process. They also used the GPSS to conduct
the demographic and employment characteristics analyses, mixed land-use
24
measures, analyses of pedestrian accessibility to transit and pedestrian
environment factors, and mapping and displays of model outputs.
Figure 3.1: GIS-based Urban Transportation Planning System in Portland
Metro, Oregon - Households, Employment Sites, and Activity Centers
Source: U.S. DOT, 1998.
The GPSS increasingly and continually assisted the planning activities in
the improvement of the travel-modeling program at Portland Metro, Oregon. The
system enabled planners to enhance travel models with more quantitative
databases and collect and organize consistent information for analysis and
validation. They benefited from the technical support provided by this
comprehensive regional GPSS that allowed them to advance their travelmodeling efforts. This GPSS applied in Portland Metro demonstrated to planners
how GIS could be used to support urban transportation planning. GIS databases
provided an effective system for data management, and the GIS contained a
number of tools for spatial analysis and data display that added value to the
modeling process. Inthe future, GIS is likely to play an increasing role in
transportation planning and become an important technology to shape the
system framework and model development.
GIS-Based Planning Decision Support System for Transportation Planning
and Infrastructure Management, Bolivia (GAF mbH Company, 1998)
The governments of Argentina, Bolivia, Brazil, Chile, Paraguay, and Peru
worked together on projects of physical integration, so that the objectives of this
project were to develop and implement a GIS -based planning system to support
multi-modal transportation studies. The main objectives of the project were to
develop the Bolivia highway transportation master plan and the transport
alternatives of South-American inter-oceanic corridors, which start from central
west Brazil and Bolivia and are destined to the Pacific Basin market.
Figure 3.2: Inter-Oceanic Corridor Passing Through the Republic of Bolivia
Source: GAF mbH Company, 1998
The project also included a highway inventory system using a satellitebased Geo-referenced Positioning System (GPS) technology. The planners
conducted alternative analyses of potential volumes of traffic between the study
area (Central West Brazil and Bolivia) and Pacific Basin markets through Pacific
coast ports. They used the system as a platform and framework to update
existing exports to the Pacific Basin and corresponding transport infrastructure
needs from ports, rail and highways, while eventually strengthening sub-regional
cooperation among the participating countries.
Inthis process, GIS technologies enhanced the study by making available
further analytical tools, and providing planning and management capabilities. By
using a GIS-based support system, an analyst can use more efficient and costsaving methods than with any alternative approach to update the transportation
information in the studies of outlets to the Pacific from the inner regions of
Central South American. Also, it is easier for the analyst to bring new
infrastructure and technological developments and changes of productive
capacity into the decision environment. This GPSS provided analysts GIS-type
capabilities for the transportation sectors and a comprehensive spatial database,
which they used primarily for project planning and infrastructure management.
GIS also provided the means for analysts to visualize the spatial aspects of the
problem being studied and a number of additional analytical tools. The analysts
also can employ GIS -related technologies and planning capability in other
studies for this region.
* GIS-based Planning Support System for Human Ecology and Environmental
Studies (CUEH, University of Geneva, 1998)
Figure 3.3: The AIDAIR System
GIS-DataBase
'Economic Acivity
Ene19
I
Topography
Trnspot teh. iTraffic
Polluttinndispersonn
Source: CUEH, University of Geneva, 1998.
AIDAIR is a GIS -based decision-support system for air-pollution
management in an urban environment. It is built around the AIRWARE, a
product developed by the Austrian company Environmental Software and
Services. Inthe AIDAIR-GENEVA project, policy makers in the Geneva region
used AIDAIR system in their assessment of public policies and strategies
concerning urban air-quality management.
This system is composed of three interconnected modules: EGIS (Energy
GIS), TAP (Traffic and Air Pollution) and APPH (Air Pollution and Public Health).
The system structure and the relations among the three modules are represented
in Figure 3.3. EGIS is based on a technical-economic model of energy and
technology choices. This module displays the results from the model by the GIS
maps and integrates the energy technology choices directly in the APPH module,
which simulates atmospheric pollutants emissions and dispersion. Figure 3.4
shows a simulation of Nitrogen Oxide(s) emissions from industrial sources in the
Geneva region.
Figure 3.4: NOx Industrial Emissions Plumes on a Fresh Northeasterly Windy
Day in Geneva
Source: CUEH, University of Geneva, 1998.
The TAP module (Traffic and Air Pollution) is based on a traffic equilibrium
model inthe AIDAIR system, which computes the air-pollution emissions due to
traffic. From the traffic loads computed by the traffic equilibrium model, the TAP
module estimated and displayed the emissions of pollutants from traffic activities
(Figure 3.5). Analysts can use the APPH module to model the atmosphericpollution-dispersion and to apply epidemiological studies on air-pollution effects
to the population of the canton of Geneva. Concerning impacts of air pollution on
public health, the AIDAIR system takes suspended particulates (PM10) as
pollution indicators. As these PM10 emissions are not measured in Geneva,
researchers inferred them from the NOx emissions. The epidemiological analysts
tried to show a relationship between disease prevalence and different airpollution levels.
Figure 3.5: NOx Emissions Due to Traffic in Geneva Region
&~
~M
~ ~m
~eINENEEPA
P
LE TRARC
Source: CUEH, University of Geneva, 1998.
* Summary
From my reviews of the GIS -based planning decision-support systems,
there are several common elements in these systems besides accurate spatial
analysis from GIS tools:
1) GPSS enhances the efficiency and quality of alternative analyses in the
planning and program development. It is both time-saving and cost-saving to
conduct altemative analyses by GIS technologies in the motor-vehicle choice
analysis inthe Portland Metro urban-planning studies and traffic-volume
analysis of inter-oceanic corridors in the Bolivia case.
2) To conduct specific studies, GPSS links professional models in the system,
such as a travel-demand-forecasting model in the Portland Metro case, an
energy-technology-choice model, pollution-dispersion model and healthimpact model in the AIDAIR system. Those professional models expand the
GIS functions and make GPSS applicable for solving real-world problems.
3) GPSS contains a comprehensive database shared by different modules and
models in the system. In the Bolivia case, the sharing of the GPSS database
with the updated information of exports and corresponding infrastructure
needs undoubtedly strengthened sub-regional cooperation among the
participating countries. In the AIDAIR system, the model integration is based
on the comprehensive database for the whole Geneva region, which includes
statistical socio-economical data, traffic data, emission data, and
meteorological data.
4) GPSS effectively maps and displays model outputs and makes results more
observed than before by using GIS technologies. The AIDAIR system
visualizes the model results and links them with other demographic data for
further analysis. The overlay of NOx emissions with the population
distribution clearly indicates visually the exposure of the Geneva population to
health risks.
The SPGPSS I developed for transportation and industrial-location
analyses of the cokemaking sector in Shanxi Province has the above
characteristics, but it is distinct in the some aspects. Usually, GPSS needs to
have a large database with multi-area information to support its integrated
modeling work. Inthe AIDAIR-Geneva project, the comprehensive and shared
database made it possible for the analysts to integrate different professional
models in the system and conduct advanced analyses between pollution
emissions and health risks. Inthe SPGPSS, I need considerable data for typical
transportation and environmental studies, which are not available due to the
limitation of the data collection in China. I therefore designed the SPGPSS for
two levels of research with different data requirements.
I did the first-level research work on the provincial level. Based on the
basic information of the transportation network and cokemaking plants, my
colleagues and I calculated the transport cost, energy consumption, and pollution
emissions for each transportation link using the formulae and parameters we
collected during the interviews and research, which to some extent made up for
the lack of original detailed data. By connecting with the NETFLOW optimization
model, an analyst only needs to know the transport cost and capacity of each link
in order to have the SPGPSS conduct network optimization and generate the
best transport routes and flows to minimize total transport cost. The analyst can
also use the same method to optimize the transport routes and flows for energy
consumption and pollution emissions. I ran the SPGPSS to conduct alternative
analyses and compared the results to examine the impacts from the changes in
prerequisites. Taking advantage of the efficiency and quality of SPGPSS in the
alternative analyses, I focused more on the relative changes between
alternatives than on the absolute results of each alternative and explored the
reasons behind those changes.
The second-level research is on the plant level. Our AGS MRP research
group did specific and detailed surveys in some Town and Villages Enterprises
(TVEs) and State-Owned Enterprises (SOEs) in the cokemaking industry in
Shanxi Province. I located those plants in the GIS transportation network by the
addresses in the surveys. With the detailed information from the surveys, I used
spatial tools imbedded in Arcview GIS to do spatial analyses and multi-plan
valuations for an individual plant in the planning of transport routes and new plant
location. My approach is to rely on the GIS spatial tools to conduct the
comparison between the different transportation and location plans for
cokemaking plants and to let coke managers themselves choose the best plan.
3.2 Transportation NETFLOW Model
I applied the freeware "NETFLOW" (Kennington and Helgason, 1980) to
the optimization of transport routes and flows in the SPGPSS. NETFLOW is a
mathematical optimization model to solve the cost-minimization problem of
transporting a given quantity of material from a single-supply node to a singledemand node across a network of links and nodes. Each link has an associated
cost and capacity. The program minimizes the total cost of transport across the
network, subject to the capacities of each link. An analyst also can use it to
minimize the total cost, energy consumption or pollution emission across the
transport network.
Kraines and Akatsuka in Tokyo University first applied this model to the
transportation study of cokemaking sector in Shanxi Province. They constructed
coal and coke transportation networks expressing the entire transportation
infrastructure in Shanxi Province, using the cost-converted GIS-link data as
described by Akatsuka (Akatsuka, 2001). Then, they connected the coal network
and coke network at each cokemaking plant node with a link having the plant's
production capacity as the link capacity and zero link cost. To satisfy the
requirement of the NETFLOW program, they also created the single-supply node,
single-consumption node, the links between single-supply node and the nodes
for each coalmine, and the links between single-consumption node and the
nodes for each coke consumer. They set these links with capacity equal to the
coalmine production capacity or the coke-user consumption requirement and
cost equal to zero. After setting the capacity and cost for each link, they ran the
NETFLOW program to do the transportation-flow and cost-minimization
calculation and got the minimized total cost of transport across the network and
the optimized flow route.
It is a very successful example of how to apply a mathematical
optimization model to the real-world problem solving. They used the Excel
spreadsheets as a database and Java-developed Graphic User Interface (GUI)
to visualize the optimal routes and plant scales. But with a separate database
and GUI, users have difficulty of updating the information and operating the
program. Also the Java-developed GUI does not show as accurate spatial
information as professional GIS software. For advanced spatial analysis, the
SPGPSS has major advantages with many specific-designed GIS spatialanalysis tools.
3.3 Process-Flow Model
I connected a modified version of the input-output process model (IOPM)
developed by Lin and Polenske with the GIS environment. Based on the survey
data, the cokemaking process-flow model can provide a great deal of information
for each facility within a plant and the interrelationships among the facilities that
comprise this type of coal-using enterprise. In terms of costs, mass flows, and
environmental emissions, I used the IOPM to evaluate alternative cokemaking
technologies and distinguish the differences between them. In the process-flow
balance sheet, the rows contain four key sections: main products, purchase
inputs, byproducts and wastes, and primary inputs representing financial costs.
The columns pertain to the seven facilities in the plant: (1)steam coal washer, (2)
metallurgical coal washer, (3)producer-gas generator, (4) steam coal-fired
electric power plant and (5-7) three types of ovens used at the hypothetical plant.
This balance sheet provides information on the investment, labor, and
maintenance costs plus costs of purchased coal and utilities costs. It also
estimates revenues obtained from the sales of coke, chemical by-products, and
generated steam and electricity (Polenske and McMichael, 2002).
3.4 Industrial-Location Theories
In their book "Urban Economics and Real Estate Markets", DiPasquale
and Wheaton (1996) illustrated that the pattern of spatial separation and location
of different land uses in the urban area is due to their different land-rent gradients
(Figure 3.6). The industrial uses have a relatively flat land-rent gradient and
require lower land rent than the residential and commercial uses, which resulted
in the industrial firms largely locating in suburban, even rural, areas. To some
extent, the flat land-rent gradient for industrial use is due to the extensive land
use of industrial firms and the lower land rent they are able to afford than the
other uses. Due to the spatially diffuse character of truck and rail transportation,
industrial plants decentralize, because they are less willing than others to
compete with denser land users with higher rent, such as residential,
commercial, and retail users.
Figure 3.6: Different Land-Rent Gradients
Land Rent
Industries
CBD
------------
Residenc
-----------------------------------------------------
Distance
Source: DiPasquale and Wheaton, 1996.
From the twentieth century, DiPasquale and Wheaton (1996) indicate that
the changes in both production and storage methods greatly increased the
amount of land used per unit of output by industrial firms. The integrated
horizontal assembly lines and modern inventory technology both have high land
requirements and increased the amount of land needed for the industrial use.
Thus, they argue that the production and technology requirements have driven
the industrial use, consuming more land than the other users. Even in the same
industry, the industrial firms with different production technologies have the
different requirements of land-use intensity, which eventually causes those firms
to have different land-rent gradients and choose the different locations.
With the suburban decentralization of households, many firms are
decentralizing to be closer to their workforce, which consequently lets them pay
lower wages and receive more profits (DiPasquale and Wheaton, 1996).
Employment is one of the factors for decentralization of the industrial firms, but I
argue that employment is not the determining reason for the decentralization of
industrial firms in developing countries, such as China. As we know, China has a
large population and plentiful labor, so that the workforce is not a driving factor
for the firms to decide where they locate to reduce the cost. Comparatively, the
transportation cost is a more important factor determining the industrial locations.
Industrial plants prefer to locate near the raw-material resources, suppliers,
customers, and major transportation routes to reduce their transportation cost.
They locate and decentralize with the attraction of the easy access to
suppliers/customers and major transportation network. Thus, in the conditions
that labor cost is not as important as transportation cost, the industrial-location
choice is not employment-driven, but transportation-driven.
Industrial firms have a tendency to locate by merging and by reaping
agglomeration economies. Rees and Stafford (1986) indicate that firms might
derive economic advantages from locating in larger, more central, clusters.
There is a fundamental tension between economies of scales and the impact of
distance. Larger economies of scale result in larger, fewer, more widely
separated plants. Smaller plants located in a finer, more dispersed spatial
network would have low friction of distance-related (transportation) costs, but
higher investment cost in total. Larger plants may be internally more efficient,
and, in the aggregate, easier to manage; but transportation costs are higher,
single investments are larger, flexibility is reduced, and the risks of a poor
locational choice are greater. The trick is to balance these opportunity forces
correctly (Rees and Stafford, 1986).
How to reach the optimization point of the decentralization and
aggregation is an interesting question. With the use of the GIS technologies and
network optimization algorithm, I conducted transportation and industrial-location
analyses to get the balancing point between decentralization and aggregation in
the case study of the coal and coke transportation of the cokemaking sector in
Shanxi Province, China.
Chapter 4
METHODOLOGY AND PROJECT DESIGN
I have designed a GIS -based Planning Support System (GPSS) with the
technologies of Geographic Information System (GIS) to assist decision--making
and policy analysis. It is now commonplace for business, government, and
academia to use GIS for many diverse applications. I apply the Shanxi Province
GPSS (SPGPSS) to transportation and location choices faced by the coke
managers and local government officials in Shanxi Province, China.
4.1 SPGPSS Components and Organization
GIS is a computer-based system capable of holding and using data
describing places on the earth's surface. "GIS doesn't hold maps or pictures - it
holds a database. The database concept is central to a GIS and is the main
difference between a GIS and drafting or computer mapping systems, which can
only produce good graphic output" (ESRI Inc., 1997). GIS gives users a powerful
analytical tool to combine both graphic maps and linked attribute data, such as
population, property value, and pollution emissions, into one integrated system.
It also allows users to build their own analyses and query models connected with
the GIS system, and it automatically generates final results for alternatives. In
my study, GIS plays a crucial role by providing a database, a map viewer, and
analytical tools. I use the GIS package ArcView GIS developed by
Environmental Systems Research Institute Inc. (ESRI Inc.) as the major platform
for my research.
4.1.1 SPGPSS Components
The SPGPSS has four major components: (1)database, (2) map viewer,
(3)scripts, and (4) professional models. Although I use it for a study of the
cokemaking sector in China, I designed it to be applicable to other sectors and
other countries. Each component has its special functions and is independent of
other components, but they are closely connected and the basic structure of this
SPGPSS is shown in Figure 4.1.
Figure 4.1: Structure of SPGPSS
SPGPSS in ArcView GIS
Source: Author
(1) Database -Core of SPGPSS
Spatial data are at the heart of every ArcView GIS application. Spatial
data are geographic data that store the geometric location of particular features,
along with attribute information describing what these features represent, also
known as digital-map or digital-cartographic data (ESRI lnc, 1997). The spatial
data have three forms in the SPGPSS. (1)A point data set contains data of
"node" features, such as cities, towns, villages, and cokemaking plants, as well
as their attribute data, such as city population and plant-production rates. (2)A
line data set contains the data of "links", representing primarily the roads and
railways of the transportation network. (3)An area data set mainly stores data of
regions, such as industrial districts, residential areas, and even cities, towns, and
villages. Except for the spatial data, the non-spatial attribute data set stores
information with numerical and characteristic values, like numbers and qualitative
evaluations. Spatial data and non-spatial data sets can contain the information
users need to run spatial models and make the specific quantitative and
qualitative analyses. I prepared and integrated the data sets to form the major
database for the SPGPSS for Shanxi Province cokemaking sector, which
contains economic and technological data of mines, cokemaking plants, and
consumers in the transportation network.
In this SPGPSS, most of the spatial data are stored in the form of ArcView
shapefiles. The shapefile format has five files with specific file extensions, of
which the most useful two files are the .shp file - the shape file that stores the
feature geometry, and the .dbf file - the dBASE file that stores the attribute
information of features. Users can add a shape file (.shp) into a map viewer as
a theme, which represents all the features of a particular feature class in the data
source. They can then display the related dBASE file as a feature table in
ArcView. They can export the dBASE file into Excel and other databaseprocessing software and also import the dBASE file from Excel to Arcview by the
function of adding tables. The dBASE file is one kind of attribute data, which can
include almost any data set, whether or not it contains geographic data. Users
can display some tabular tables in a map viewer directly, and they can join others
that provide additional attributes to the existing spatial data. ArcView can
support the data from database servers, such as Oracle, dBASE III and IV files,
INFO tables, and text files with fields separated by tabs or commas.
(2) Map Viewer-Graphic User Interface (GUI) of SPGPSS
The ArcView GIS can have different spatial data stored in the different
views, which provides users with a separate layer to display and query a
collection of user-defined themes. By displaying or hiding those views, users can
have the different combinations of the spatial data for analyses. Below, I provide
two views with different theme combinations, showing the different parts of the
supply chain in the cokemaking sector in Shanxi Province. Inthe view of coal
transportation (from coalmines to cokemaking plants), I include themes of
coalmines, cokemaking plants, railways, roads categorized by the service levels,
cities and towns, and the border of Shanxi Province (Figure 4.2). Inthe view of
coke transportation (Figure 4.3), I turn on the theme of coke consumers and turn
off the theme of coalmines, and then the view shows the coke-transportation
routes from the cokemaking plants to the coke consumers.
Figure 4.2: View of Coal Transportation in Shanxi Province, China
I
Source: Author
Figure 4.3: View of Coke Transportation in Shanxi Province, China
Source: Author
By using Arcview tools, users can move or zoom the views in and out
conveniently. When users select an object inthe view, they can select and
highlight the data stored in the related feature table, as shown in Figure 4.4. The
automatic link between the map in the view and the data in the feature table is a
great advantage in spatial analyses, because users do not need to switch back
and forth between the different programs, and they can show all the changes and
updates in the feature table in the corresponding map. Users can use the GIS
environment to combine spatial data as well as corresponding non-spatial data.
Such a combination overcomes the traditional bottleneck between maps and
related tabular data. The map viewer works as a Graphic User Interface (GUI)
for the SPGPSS. By adding additional buttons and menus for specific users, I
customize and design a user-friendly GUI.
Figure 4.4: ArcView's Automatic Links between Map and Database
Source: Author
(3) SCRIPTS - BRIDGES OF SPGPSS
Script is a piece of code written by an object-oriented language named
Avenue, which is the programming language embedded inArcView GIS. Scripts
can control how and when to send requests to function objects in ArcView.
Through Avenue scripts, users can expand the GPSS by connecting the ArcView
GIS with professional models and by customizing ArcView's look and
functionality. Inthe SPGPSS, the major functions of scripts are to retrieve the
needed data from the tables in the ArcView GIS, sort or format those data, write
the data into the professional models outside the ArcView GIS and execute those
models, read the results back from the models to the ArcView GIS, and update
the old tables.
Script works as a bridge connecting the professional models with the
ArcView GIS and integrates the different components into one system. The
customized buttons on the toolbar and the menus of map viewer can link those
scripts. Users only need to click those buttons or menus to start those scripts to
run the professional models inthe background and then get the results back to
the SPGPSS automatically. These designs are especially beneficial for the
analysis of alternatives. By changing the data in the maps or tables for different
alternatives, users can run models efficiently and get results quickly for
comparison and analysis.
(4) Professional Models-Branches of SPGPSS
Inthe SPGPSS, I connect the system with two professional models: the
transportation NETFLOW model (Kraines et al., 2001) and the process-flow
model (Polenske and McMichael, 2002). I introduced these two models in the
literature review.
These two models can share a common database in the system and
retrieve the data needed for their requirements by the scripts. Users can
conveniently start the models using a GUI, making the models run in the
background, and get the results back to the SPGPSS as soon as the models
finish their internal computations. The professional models also can be other
models in other areas, such as emission models for environment studies or
logistics models for supply-chain management. The type of model that can be
connected depends on the database design of SPGPSS and the connection
interface of the model with the system. Some models have very specific data
requirements, for example, the emissions model for transportation studies usually
need emission-factor data, which relate to the data of vehicle type, speed, and
traffic volume. To implement such models, users must incorporate such a
dataset into the SPGPSS database and resolve the connection requirements for
each model.
4.1.2 SPGPSS Advantages
As noted in Section 3.2, Kraines and Akatsuka successfully modeled the
transportation activities of the cokemaking sector in Shanxi Province. They used
Excel spreadsheets as a database and Java-developed Graphic User Interface
(GUI) to visualize the optimal routes and plant scales. Compared with the
system developed by Kraines and Akatsuka (K&A System), the SPGPSS has the
following advantages for spatial-information processing and alternative analyses
as an integrated system with many powerful components (Figure 4.5).
Figure 4.5: Comparison between the SPGPSS and System Developed by
Kraines and Akatsuka (K&A System)
SPGPSS
K&A System
Source: Author
1) Integration: The SPGPSS integrates the database, GUI, and professional
models in the GIS ArcView Environment. These components are
automatically linked with each other in the system. Users can do all the
operations in one program, GIS ArcView, and do not need to switch between
the different programs, such as Mapinfo, Excel and Java-developed GUI as in
the K&A System. Thus users can avoid mistakes in the operational process.
2) Interaction: Users are able to interact with the system in a real-time mode.
Users do not need to know the complicated structure inside the SPGPSS,
which works as a black box, but they just insert different alternatives from a
friendly GUI and obtain the results quickly.
3) Accuracy: The GIS-based processing maintains data integrity and accuracy.
GIS has a great advantage for use in spatial-information processing and
analyses because of the automatic links between maps and tables. The
ArcView GIS program also provides many spatial-analysis tools, such as the
identify function and the measure function. Although the K&A System uses
another GIS software, MapInfo, contains the GIS data, but it only provides the
distance and some other data to Excel for computation, and does not conduct
spatial analysis and visualize the optimal results in the program by the GIS
technologies. Thus, for advanced spatial analysis, the SPGPSS, which has
many specific-designed GIS spatial-analysis functions, has major advantages
over the K&A System.
4.1.3 SPGPSS Organization
Figure 4.6: System-Flow Chart of SPGPSS
Source: Author
The SPGPSS is organized as shown in Figure 4.6. Users can start from
the ArcView Map Viewer (GUI). An interactive GUI is an important component of
the SPGPSS. It includes GIS special features, such as maps, tables, legends,
and common components including toolbar, menu, and application tips. The GUI
automatically interacts with related databases by the special link function of
ArcView GIS. By the tools on the menu bar and buttons of the GUI, users can
select and update the data on maps and tables. For example, users can insert a
new plant onto the map, change production data of the cokemaking plant in the
table, or update the capacity of a specific road or railway. The inserted
information will immediately update the feature tables in the database. Inthis
SPGPSS, I use scripts to connect the professional models. The scripts are
linked with the buttons on the toolbar. After users click the button, the script will
write an input data file from the system database, and tell the professional model
to read the input data and execute itself to do internal computations. Then, the
professional model gives computation results by writing an output file. The
models stay in the background of the system and do the computation work inside
a black box. The output file returns to the system database and updates the
related tables automatically by another script. Simultaneously, the maps in the
GUI spatially show the updated information.
4.2 Analysis of Alternatives
By applying this SPGPSS for the different alternatives (i.e., different inputs
in the SPGPSS database), I found that it is effective and efficient to use the
alternative analyses for this research. I compare the minimized total costs,
energy consumption, or emissions from transportation and cokemaking plants
under different alternatives in the year 2000; explore the underlying reasons
behind those differences; evaluate several coke-oven technology options for the
plants in terms of plant costs and emissions; measure the impacts of new
highway construction on the coal and coke transportation by the reduction of
costs, energy consumption, and emissions; and finally recommend the optimized
plant location, capacity allocation, transportation routes and modes by comparing
the results from these alternatives. Kraines and Akatsuka first developed this
method of alternative analysis and used the Base Scenario, TransportationMinimization Scenario, and Plant-Minimization Scenario based on the 1990 data
in their previous transportation studies in Shanxi Province (Kraines et al., 2000).
I keep using these scenarios to make our research consistent. Based on the
updated 2000 data of coalmines, cokemaking plants, and coke consumers, I
conduct further analyses of the following alternatives at the provincial level:
e
2000 Base Scenario (2000 Base): Minimize the transportation cost, energy
consumption, or pollution emissions and give the corresponding
transportation pattern, based on the distribution of coalmines, cokemaking
plants and coke consumers in 2000 (Shanxi Statistical Yearbook, 2001).
0 2000 Transport-Minimization Scenario (2000 Transport-Min): Set plant sites to
minimize transport cost, energy consumption, or pollution emissions. First, I
set the capacity of each plant in the base scenario to 3,000,000 tonnes per
year, which is the upper limit for individual plant coke production, not the
actual production (AGS MRP Field Trip Interview, 2002). Next, I run the
GPSS and transportation tradeoff model. This calculation gives the lowest
possible transportation cost, because each plant is allowed to produce under
the maximum capacity. Then, I change the production capacity of each plant
just exceeding the production rates required by the transportation tradeoff
model. I also use the plant model to calculate the cost, energy consumption,
or pollution emissions from cokemaking process and add them to the
transport part to give the total cost, energy consumption, or pollution
emissions. Therefore, this scenario gives the minimum transportation cost,
energy consumption, or pollution emissions in the supply of the total coke
demand given the locations and capacities of coalmines, the possible
locations for cokemaking plants, and the maximum cokemaking plant size
(Kraines et al., 2000).
* 2000 Plant-Minimization Scenario (2000 Plant-Min): Set plant sites to
minimize plant cost, energy consumption, or pollution emissions. In this
scenario, I set the plants to maximum capacity and run the SPGPSS. I also
calculate the number of maximum capacity (3,000,000 tonne per year) plants
required to produce the total coke demand in Shanxi Province. Then, I assign
one of these maximum-sized plants to the site having the largest production
capacity calculated by the transportation-tradeoff model. I continue to assign
maximum-sized plants to the sites with the largest calculated production
capacity until I have assigned the total required number of plants. I assign the
last plant a production capacity just large enough to meet the total demand. I
then run the SPGPSS again with these location conditions and calculate the
plant costs, energy consumption, and/or pollution emissions using the by the
plant process-flow model. This plant-minimization scenario is the simulation
in which small-scattered plants are closed and replaced by aggregated bigscale plants (Kraines et al., 2000).
* Coke-Oven Technology Impact Analysis:
After the implementation of the 1997 #367 national environmental regulation,
there are two major recommended coke-oven technologies, large-machinery
coke-oven technology and nonrecovery coke-oven technology. I therefore
give three options for coke-oven technology adoption:
Option 1: All the plants use the large-machinery coke-oven technology,
Option 2: All the plants use the nonrecovery coke-oven technology,
Option 3: All the plants whose capacity is more than or equal to 500,000
tonnes use large-machinery coke-oven technology; all the plants whose
capacity is less than 500,000 tonnes use nonrecovery coke-oven
technology.
* New Highway Construction and Speed Improvement Impact Analysis: the
impacts from the newly built highways and speed increase from 1990 to 2000.
* Industrial Parks Location Analysis: Infuture provincial planning, the Shanxi
government officials plan to develop two cokemaking industrial zones. The
tentative locations are in the Lishi-Liulin area, Linfen area, or Jiexiu area.
Using this SPGPSS, I make simulations of the establishment of these
industrial zones and compare the total cost, energy-consumption, and
pollution emissions from different choices of the industrial zones. I begin with
three location alternatives, each with two of the three industrial zones, and I
assume that each industrial zone will produce half of the current total coke
production in Shanxi:
1. Lishi-Liulin industrial zone and Linfen industrial zone.
2. Linfen Industrial zone and Jiexiu industrial zone.
3. Lishi-Liulin industrial zone and Jiexiu industrial zone.
4.3 Case Study
At the plant level, I do a case study of an individual cokemaking plant in
Shanxi Province. Ifocus on how a coke-plant manager can utilize the SPGPSS
to conduct the valuation and comparison of the multiple plans for the plant's
future operation and management, which includes selection of the location for a
new plant, the potential suppliers, the transport routes and modes, and the cokeoven technologies.
For the individual plant manager, it may be inappropriate to use the
SPGPSS to do the network optimization by the transportation net-flow model, but
plant managers still can utilize the analytical tools in ArcView GIS and its
database linked with the professional model, such as the process-flow model, to
do specific spatial analyses and multi-plan valuations.
Chapter 5
IMPLEMENTATION
Inthe implementation of this Shanxi Province GIS -based Planning
Support System (SPGPSS), my colleagues and I collected the related data and
prepared the database for the SPGPSS. I also did the work of GIS modeling,
programming and processing for this system.
5.1 Data Collection and Database Creation
Our research team obtained the current GIS maps of Shanxi Province
from the Australian Center of the Asian Spatial Information and Analysis Network
(ACASIAN), including the spatial data of cities and towns, roads and railways in
Shanxi Province. I thank Crissman in ACASIAN, who provided us these GIS
maps. Kraines and Akatsuka first started the transportation studies in Shanxi
Province and gave me suggestions for my further work. They inserted into the
Shanxi Province GIS maps the locations of major coalmines, cokemaking plants,
and coke consumers in Shanxi Province and the production, consumption, and
export data as well as the capacities of major railways from the 1990 Energy
Resources Atlas of Shanxi Province (Shanxi Committee of Atlas Compilation,
1994). They also developed the formulae to calculate the transportation cost,
energy consumption, and NOx emissions for each transportation link in the GIS
system (Appendix A, Kraines et al., 2001). They saved those data in Microsoft
Excel spreadsheets.
To create the SPGPSS and make the original work more GIS -based, I
transferred the GIS data from MapInfo software to Arcview, the platform of the
SPGPSS. MapInfo and ArcView are both GIS software and widely used in the
GIS applications. I chose ArcView as the platform of the SPGPSS because
Arcview has more powerful functions and convenient tools to connect other
professional models with the system itself than Mapinfo, which is an outstanding
advantage for the creation of a GPSS. Arcview can read the GIS data in the
format of Mapinfo and save them into the Arcview data format.
Inthe current Shanxi GIS map, I located the 107 Town and Village
Entrepreneurships (TVEs) and 8 State-Owned Entrepreneurships (SOEs) from
the Alliance for Global Sustainability (AGS) Multiregional Group (MRP) 2000 TVE
Survey and 1999 SOE Survey (AGS MRP team, 2001) using a Shanxi Province
Atlas (Shanxi Transportation Facilities Office, 2001). InArcView, I pasted the
scanned maps from the Atlas to the background and located the selected TVEs
and SOEs by the detailed addresses provided in the surveys.
Because the surveys contain confidential information, I located the plants
by their addresses only for research and internal use. By assigning each plant a
unique IDin the SPGPSS, I exclude the plant name and any other information of
identification from the SPGPSS. With the unique IDfor each plant, I can join the
plant's spatial data with the plant's operational and investment data from the
2000 TVE survey and 1999 SOE survey, which include the information of coke
and by-product production, suppliers, consumers, transportation, facility and
equipment, financing, employment, and pollution. To integrate the plant-specific
data from the surveys into the SPGPSS, the plant managers and other users can
do the different aspects of analysis of an individual plant according to their goals
and needs.
Table 5.1: Coal Production, Coke Production Capacity, and Coke Consumption
in Shanxi Province, 1990 and 2000
Annual
increase over
10 years
2000
1990
27.6%
61,618,000 169,882,000
Total Coal Production for cokemaking
35.3%
16,100,050 56,913,000
Total Coke Production Capacity
22.8%
14,599,890 33,295,000
Total Coke Consumption
# Unit: tonnes/year
Note: Total coke consumption includes consumption in Shanxi Province and exports.
Source: 2001 Shanxi Statistical Yearbook, Shanxi Statistical Bureau.
From 1990 to 2000, the total coal use for cokemaking in Shanxi Province
increased 276% (27.6% annually), the total coke production capacity increased
353% (35.3% annually), and the total coke consumption (including the provincial
consumption and exports) increased by 228% (22.8% annually). During the 10
years, the Shanxi cokemaking industry has grown rapidly. I updated the
coalmine production data used in cokemaking, cokemaking plant production
capacity, consumption of coke users and exports in the SPGPSS by the 2000
data from Shanxi Fifty Years 1949-1999 (China Statistics Press, 2000).
For the transportation model, I use the formulae and transportation link
data for fuel efficiency, cost, energy consumption, and NOx emission from
Kraines and Akatsuka (Akatsuka, 2001). The high pollution from diesel trucks,
especially the particulate pollution aroused our attention during our field trips in
Shanxi Province. Inthe interviews with Shanxi Environmental Protection Bureau
(EPB) officials, I found that they realized the transportation pollution is another
significant source of pollution, but they have not given it much attention yet
because of the difficulties to make reliable measurements and conduct
quantitative analyses. Because transportation pollution is a major concern from
our research perspective and is also another important factor affecting the
relocation and merger potential of cokemaking plants, I added the Particulate
Matter (PM) and Sulfur Dioxide(s) (SOx) emission indicators to measure the
transportation emissions. I use the vehicular emission factors with respect to fuel
consumption in the Philippines (Rogers et al., 1997) to estimate the PM and SOx
emissions. I have not found such vehicular emission factors in China that I can
use for the formulae. Because China and Philippines are both developing
countries and have similar pollution-emission conditions, I use the Philippines
vehicular-emissions factors in these pollution estimates. For the diesel fuel, the
vehicular-emission factor of PM is 18.0 gram/liter and the vehicular emission
factor of SOx is 10.8 gram/liter. I get PM and SOx emissions in units of grams
for transporting one tonne of coke by each transportation link as follows
(Equation 1):
E, =EFxEff xD
(1)
E,: Transportation Emission (gram/tonne)
EF: Emission Factor (gram/liter)
Eff: Fuel Efficiency (liter/tonne-kilometer)
D: Distance (kilometer)
For the plant process-flow model, I develop the formulae to calculate the
plant cost, energy consumption, and pollution emissions. To calculate the total
plant emissions, I multiply the average emissions to produce one tonne of coke
by the total coke production by those plants in that year (Equation 2):
E, = AVG(E,)x TP
(2)
E,: Plant Emission (kilogram/year)
AVG(E,): Average Emission per tonne of coke (kilogram/tonne)
TP: Total Coke Production (tonne/year)
I calculate the PM plant emission based on the average number of PM
emissions permitted to be released for producing one-tonne coke in Shanxi
Province. From our interviews in the Shanxi Environmental Protection Bureau
(EPB), I learned that the average PM emissions permitted by the EPB is 1
kilogram per tonne of coke for the non-recovery coke-oven technology, and 2.5
kilogram per tonne of coke for the large-machinery coke-oven technology.
Assuming that all plants emit only the regulated maximum, I determine that for
the total coke production of 33,295,000 tonnes in year 2000, the total annual PM
emissions is 33,295,000 kilograms if all the plants use the non-recovery
technology, or 83,237,500 kilograms if all the plants use the large-machinery
technology. This, of course, is probably less than what was actually emitted,
because some plants were not in compliance with the regulations in the year
2000. The average SOx emissions permitted by the EPB is 1.8 kilogram per
tonne of coke produced for the non-recovery technology, and 0.4 kilogram per
tonne of coke produced for the large-machinery technology (AGS MRP Field Trip
Interview, 2002). By the same method as above, I determine that the total SOx
emission is 59,931,000 kilograms if all the plants use the non-recovery
technology, or 13,318,000 kilograms if all the plants use the large-machinery
technology. Because of the same total coke production, the PM and SOx
emissions from cokemaking plants are the same in the three scenarios.
For the total plant cost, I sum the plant costs of all the cokemaking plants.
I calculate the three plant-cost components, operational cost, investment cost,
and land cost, for each plant and then add them. Based on the economic model
(Chen, 2000) to estimate the operational cost and investment cost of different
cokemaking technologies in Shanxi and the modeling of "economies of scale" of
cokemaking technologies (Kraines et al., 2001), I use Equations (3) and (4)to
calculate the operational and investment cost for non-recovery coke-oven
technology and Equations (5) and (6)for large-machinery technology.
For non-recovery coke-oven plants:
(3)
Operational Cost = 3.0 x (0.9x PS/BPS + 0.1) + 0.16 x P
Investment Cost = 1.35 x (0.9 PS/BPS + 0.1)
(4)
For large-machinery coke-oven plants:
(5)
Operational cost = 19.32 x (0.9 x PS/BPS + 0.1) + 0.57x P
Investment Cost = 36.81 x (0.9 PS/BPS + 0.1)
(6)
Cost Unit: Million RMB per year
PS: Plant Scale
BPS: Base Plant Scale (300,000 tonnes per year)
P: Production of Coke
Inthe previous work done by our team, the land cost has not been taken
into consideration. On our field trips, I noticed that non-recovery technology
consumes more land than large-machinery technology, although the operational
cost and investment cost of non-recovery coke-oven technology is much lower
than that of large-machinery technology. From the interviews, I know that the
average annual opportunity cost of the land for farming in Shanxi is 700 RMB
($ 84.3) per Mu. 3 For a typical non-recovery coke-oven plant of 500,000 tonnes
per year capacity, the annual opportunity cost of the land of this plant used for
farming is therefore 56,000 RMB ($6,747). Considering the "economies of scale"
of cokemaking technologies (Kraines et al., 2001), I use Equation (7)to estimate
the land cost of non-recovery coke-oven plant. For a large-machinery coke-oven
plant of 1,230,000 tonnes per year capacity, the annual opportunity cost of the
land of this plant used for farming is 84,000 RMB ($10,120). I use Equation (8)
to estimate the land cost of large-machinery coke-oven plant.
For non-recovery coke-oven plants:
Annual land cost (RMB) = 56,000 x (0.9 x PS/500,000 + 0.1)
(7)
For large-machinery coke-oven plants:
Annual land cost (RMB) = 84,000 x (0.9 x PS/1,230,000 + 0.1)
(8)
PS: Plant Scale
5.2 GIS Modeling, Programming and Processing
To connect different parts of the SPGPSS, I need to consider the
database processing and model programming. To incorporate the professional
models into the SPGPSS system, I process the SPGPSS database to match the
data requirements of the professional models. The transportation NETFLOW
model needs to read six specific items of data to do the optimization
3 RMB
is the Chinese currency unit. 1 RMB = 0.121 US Dollar. Mu is the Chinese area unit for
land, 1 Mu = 0.165 Acre.
computation, so that the SPGPSS database should have the link name, fromnode, to-node, transportation cost, capacity (upper bound) and lower bound for
each transportation link. Inaddition to those six items of data, I also put the
transportation energy consumption and emission data for each transportation link
into the SPGPSS database. I calculated those data from the transportation
formulae presented in Section 5.1.
After processing the SPGPSS database for the specific professional
models, I write the scripts (some paragraph of programming code to implement
some specific functions) by ArcView's programming language, Avenue. The first
script orders the SPGPSS to retrieve the needed data from the database stored
in the SPGPSS, format the data according to the requirement of professional
model, and write the data into the model, and execute the model. The
transportation NETFLOW model generates the optimized result in a data file,
which gives columns of numbers to tell which transportation links should have
how many transportation flows. Although users can get the optimized results
from this data file, it is very difficult for users to understand those results without
showing these transportation routes and flows in GIS maps. The second script
therefore reads the results back to the system and update the old database. The
maps in the SPGPSS viewers automatically show the updates and changes in
database. Script programming is a very important step in the whole SPGPSS
creation. Only by those scripts can the SPGPSS utilize the professional models
to implement some complicated tasks.
With the results generated by the professional models, users can conduct
further spatial analyses with the SPGPSS by using the tools embedded in the
ArcView software. By turning on or off the different themes with different spatial
data, such as transportation by roads or by railways, users can clearly see the
transportation flows carrying by road or by railway. With the identify tool, users
can select any transportation link from the map and obtain from a table the
transportation cost, energy consumption, and emissions of this link. From the
legends by transportation flows, users can easily see from the map viewer how
many transportation flows are carried by each transportation link.
Chapter 6
APPLICATIONS
This chapter shows the results and analyses from the applications of
SPGPSS. According to the different data requirements, I designed SPGPSS
specifically for two levels of research. On the first level, the provincial level, I run
the SPGPSS to conduct alternative analyses and compared the results to
examine the impacts from the changes in prerequisites. Ifocus more on the
relative changes between alternatives than on the absolute results of each
alternative and explore the reasons behind those changes. On the second level,
the plant level, I use the detailed survey data and SPGPSS to conduct valuations
and comparisons on the choices of location, transport routes and modes and
coke-oven technologies for the individual cokemaking plant in Shanxi Province.
6.1 Analysis of Alternatives at the Provincial Level
Based on the year 2000 production and distribution data of coalmines,
cokemaking plants, and coke consumers in Shanxi Province (China Statistics
Press, 2001), I conduct analyses of alternatives to minimize:
1) Total costs from transportation and cokemaking plants;
2) Total energy consumption from transportation and cokemaking plants;
3) Total pollution emissions (PM and SOx) from transportation and cokemaking
plants.
For each minimization, I run the SPGPSS to test the three scenarios
described in Analysis of Alternatives of Section 4.2. They are 2000 Base
Scenario, 2000 Transport-Minimization Scenario (2000 Transport-Min Scenario),
and 2000 Plant-Minimization Scenario (2000 Plant-Min Scenario). The 2000
Base Scenario provides the optimized transport routes and flows based on the
year 2000 production and distribution of coalmines, cokemaking plants and coke
consumers. The 2000 Transport-Min Scenario provides the optimized transport
routes and flows with the assumption that each plant can expand their production
capacity up to 3,000,000 tonnes per year, the upper limit for individual plant coke
production. This scenario can give the lowest transportation cost, energy
consumption or pollution emissions. The 2000 Plant-Min Scenario provides the
optimized transport routes and flows with the assumption that Shanxi Province
will have several plants of 3,000,000 tonnes per year capacity to satisfy the total
coke demand. This scenario can give the lowest plant cost, energy consumption
or pollution emissions due to the economies of scale.
6.1.1
Minimize Total Cost
By running the SPGPSS to get the optimized transportation routes and
flows for the three scenarios, I calculate the transportation cost for each scenario.
I use the plant-cost model, described in Section 5.1, to estimate the plant cost
based on plant-production data in 2000. The total cost is the summation of the
transportation cost and the plant cost.
(1)Total cost comparison
I assume that one of two cases of coke-oven technology is in effect: one
case is that all the plants are using non-recovery coke-oven technology (Table
6.1); a second case is that all the plants are using large-machinery coke-oven
technology (Table 6.2). These two coke-oven technologies are currently
considered by the local environmental officials and plant managers to be the
most promising technologies in the cokemaking industry in Shanxi Province,
although they both have advantages and disadvantages in different aspects
(AGS MRP Field Notes, 2002). To increase production capacity and reduce
environmental pollution, plant managers in Shanxi Province are adopting the
non-recovery and large-machinery technologies as the major cokemaking
technologies.
Table 6.1: Total Cost of Non-recovery Coke-oven Technology
Scenarios
2000 Base
Percentage of
2000 Transport-Min
Percentage of
2000 Plant-Min
Percentage of
Cost
total cost
Cost
total cost
Cost
total cost
1104
6456
15%
85%
574
11536
5%
95%
1341
5768
19%
81%
7560
Total cost
Unit: Million Renminbi
100%
12110
100%
7109
Transportation
cost
Plant cost
100%
Source: Author
Figure 6.1: Total Cost of Non-recovery Coke-oven Technology
Total Cost of Non-recovery Coke-oven Technology
14000
12000
10
'
MTransportation cost
10000
a
8000
N Plant cost
6000
D Total cost
4000
2000
0
2000 Base
Source: Author
2000 Transport-Min
2000 Plant-Min
Scenarios
Table 6.2: Total Cost of Large-machinery Coke-oven Technology
Scenarios
2000 Base
2000 Transport-Min
2000 Plant-Min
Percentage
Percentage
Percentage of
Cost of total cost Cost of total cost Cost
total cost
Transportation
cost
1104
3%
574
1%
1341
5%
Plant cost
33251
97%
94403
99%
26351
95%
Total cost
34355
100%
94977
100%
27692
100%
Unit: Millions Renminbi
Source: Author
Figure 6.2: Total Cost of Large-machinery Coke-oven Technology
Total Cost of Large-machinery Coke-oven Technology
100000
90000
80000
C
2
Transportation
cost
U Pant cost
cs
OTotal
3
cost
70000
60000
0
W50000
40000
30000
20000
10000
0
Scenarios
2000 Base
2000 Transport-Min
2000 Plant-Min
Source: Author
Ifind that the 2000 Plant-Min Scenario has the lowest total cost in both
cases of coke-oven technology (Tables 6.1 and 6.2, Figures 6.1and 6.2). In other
words, the Shanxi Province planners could refer to these plant locations and their
transport routes and modes in the future planning to reduce the costs both from
transportation and cokemaking process. Although the transportation cost in the
Plant-Min Scenario is the highest among the three scenarios, this scenario has a
much lower plant cost than the other two scenarios. Inthe case of non-recovery
coke-oven technology, the plant cost inthe Plant-Min Scenario is only 50% of the
plant cost in the Transport-Min Scenario, and 89% of the plant cost in the Base
Scenario.
Comparing the two coke-oven technologies, I also find that the plant cost
is 85% of the total cost inthe non-recovery case, and this percentage is even
higher in the large-machinery case (Tables 6.1 and 6.2). That is why the PlantMin Scenario has the lowest total cost due to its large savings inthe plant cost
part. We can see that the plant cost is the determining factor in the total cost due
to its much high percentage in the total cost. The scenario with the lowest plant
cost would have the lowest total cost. Because the Plant-Min Scenario has the
lowest plant cost, it finally needs the lowest total cost.
Due to the economies of scale, Ifind that the choice of fewer largecapacity plants in the Plant-Min Scenario (Figure B.1) has much lower plant costs
than more scattered small-capacity plants in the other two scenarios. Inthe
Transport-Min Scenario, the plants are dispersed to be near suppliers,
consumers, or major transportation routes to lower transportation cost, so that
the plant cost of this scenario is the highest due to highest original investment
costs of many small-scale plants in three scenarios.
From the perspective of total cost minimization, the Plant-Min Scenario is
the best, which actually supports the hypothesis that the large-capacity
cokemaking plants instead of the distributed small-capacity plants reduce the
total cost from transportation and cokemaking process.
(2)Transportation cost comparison
From the results of transportation costs, I compare the road transportation
cost with the rail transportation cost. In the 2000 Base Scenario, in terms of
transport flows, the road transportation accounts for 75% of the total transport
flow and the rail transportation accounts for 25% (Table B.1). Interms of
transport cost, the percentage of the road transport cost is 78% and the railway
transport cost is 22% of the total transportation cost. So more coal and coke are
transported by road than railway, due to the limited capacity of the railway
transportation and the convenience and directness of the road transportation
(Akatsuka, 2001). The rail transportation comprises 25% of the total transport
flows, but it only accounts for 22% of the total costs, which implies that railway
transportation is, on average, slightly less expensive than road transportation.
In the 2000 Base Scenario (Table B.2), the coal transport flow is 20% of
the total transport flows and much less than the coke transport flows, which is
80% of the total transport flows. The coal transport cost is only 27% of the total
transport cost and also less than the coke transport cost, which is 73% of the
total transport costs. Inthe optimized arrangement generated by the SPGPSS,
cokemaking plants locate nearer to the coalmines than to the coke consumers.
This pattern consequently reduces the coal transport flows and costs more than
the coke transport flows and costs. The optimized result also indicates that the
transport flows and costs can be more effectively reduced if the cokemaking
plants can be relocated nearer to the coalmines.
(3) Plant cost comparison
Inthe case of non-recovery coke-oven technology in 2000 Base scenario
(Table 6.1), I find that the determining factor of plant cost is the operational cost,
which accounts for 96% of the total plant cost. The land cost contributes a very
trivial part (0.1%) to the total plant cost. As noted earlier, I use the annual
opportunity cost of land used for farming to calculate the land cost. InChina, all
the land is owned by state. Households or enterprises only own the use right of
the land. As the cokemaking plants are usually located in the suburban and rural
areas, the land used by those plants was usually farming land before the
peasants constructed the cokemaking plant. After the economic reform in 1978,
many Town and Village Enterprises (TVEs) have emerged in the countryside.
They usually converted the land of which they own the use right from farming to
the other industrial uses. It is relatively difficult to value the use right of those
converted lands, because China is still in the process of land and property-right
reforms, and there is no established system to value the converted land and the
use rights. Because I could not obtain the data of the values of those land and
use rights, I chose to use the annual opportunity cost of the land used for farming
to calculate the cost of the use of those lands for coke plants. I assume that this
value is far too low, but I could not determine a more appropriate measure based
on the current available information.
Because of the abundant coal resources and the high quality of the coal in
Shanxi Province, the profit from cokemaking is much higher than from farming
work. That is why so much land has been converted to cokemaking use and why
many small cokemaking plants exist in Shanxi Province. In 1998, there were at
least 1500 plants in the 5-county region in which we conducted the survey. Even
today, after many plants have closed, there are at least 900 plants still in
operation (AGS MRP Field Trip Interviews, 2002). Farmers have easy access to
high-quality coal, which is often within 25-50 kilometers of their villages. With the
implementation of national environmental regulations, many local small
cokemaking plants were forced to close. These closings directly reduced the
income of TVEs and local governments. Inan interview during our 2002 field trip
in Shanxi Province, the governor of XF County (names withheld for confidential
reasons) told us that the output from the cokemaking industry was about 25% of
the total output inthis county. To implement the national environmental
regulations, the local officials closed about 230 low-quality modified indigenous
coke plants in 2001 and about 30 high-quality modified indigenous coke plants in
2002. The total loss from closing those cokemaking plants was about 500 million
RMB (about $60 million), which greatly reduced the total output and average
income of XF County. Although these forced closures can help to reduce the
local environmental pollution, the hardships incurred by the local residents
cannot be ignored.
6.1.2 Minimize Total Pollution Emissions and Energy Consumption
To estimate the minimization of the pollution emissions and energy
consumption, I run the SPGPSS under three scenarios and calculate the
transportation pollution emissions and energy consumption for each scenario. I
use the Particulate Matter (PM) and SOx as the major measurements for
emissions both from transportation and cokemaking plants. The PM refers to
particles smaller than 10 microns in diameter, and SOx refers to S02 and other
compounds in the atmosphere formed by a combination of sulfur and oxygen.
Due to the lack of the NOx data from the cokemaking process, I do not use the
NOx level as a major pollution measurement for transportation and cokemaking
plants.
The PM emissions aroused the attention of national policy makers in
recent years due to their global and regional influence on radioactive forcing and
its local effects on the environment and human health. China has high PM
emissions due to the high usage rates of coal and bio-fuels (Rogers et al., 1997).
In the latest five field trips to Shanxi Province, members of our research team
found that the PM emissions from heavy-diesel trucks are even greater than the
PM emissions from the cokemaking plants (except directly at the quenching car).
My choice of PM emissions as the major pollution measurement is therefore
appropriate when I consider estimating the pollution both from transportation and
cokemaking plants.
I calculate the PM and SOx emissions from transportation and
cokemaking plants as described in Section 5.1. Because the PM and SOx
emissions from cokemaking plants are the same in the three scenarios due to the
same total coke production, the PM and SOx emissions from transportation
determine the order of the total PM emissions in the three scenarios. The
Transport-Min Scenario gives the least PM and SOx emission (Tables B.3 and
B.4). Consequently, the total PM and SOx emissions in the Transport-Min
scenario are the least among the three scenarios. In the comparison of energy
consumption among the three scenarios, the Transport-Min Scenario also has
the lowest transportation energy consumption (Table B.5).
From the emission and energy consumption perspective, I discover that
the best scenario is the Transport-Min Scenario, which has the lowest total PM or
SOx emissions both from transportation and cokemaking plants. Compared with
the other two scenarios, the plants in the Transport-Min Scenario are distributed
to be closer to the suppliers, consumers, or major transportation routes (Figure
B.2), which effectively reduce the transportation distance, consequently, the
transportation emissions and energy consumption. This pattern actually opposes
my hypothesis that large-capacity cokemaking plants instead of the distributed
small-capacity plants will reduce the total emissions or energy consumption from
transportation and cokemaking plants. The more flexible distribution near the
supplier, consumers, and major transportation routes makes small-capacity
plants release less transportation emissions and consume less energy than the
merged large-scale plants, which cannot access the resources, markets or major
transportation routes as conveniently and efficiently as those small plants.
6.1.3 Coke-Oven-Technology Impact Analysis
The type of coke-oven technology a plant manager selects has large
impacts on the plant cost, energy consumption, and pollution emissions. As
discussed in Section 4.2, after the implementation of the 1997 # 367
environmental regulation, there are two major recommended coke-oven
technologies, large-machinery coke-oven technology and nonrecovery coke-oven
technology. I suppose three options of coke-oven technology: Option 1 is the
non--recovery technology, Option 2 is the large-machinery technology, and Option
3 is the mixture of non-recovery and large-machinery technologies. I assume
that the cokemaking plants produce the same amount of coke annually in each of
these three options.
Ifind that the plant cost in Option 1 is the lowest of the three options,
which is only about 20% percent of the plant cost inOption 2 (Table 6.3). The
major reason for this big gap is that Option 1 has much lower operational and
investment costs, although the land cost of Option 1 is higher than the land cost
with Option 2. The land cost, however, only accounts for a small part of the total
plant cost, 0.1% in the Option 1 and 0.01% in Option 2. Consequently, the plant
cost for Option 1 is still much less than that for Option 2 and Option 3.
Table 6.3: Plant-Cost Comparison of Three Coke-oven Technology Options
Plant
Operational Investment Land
Cost
Option 1 (Non-recovery technology)
Percentage of plant cost
Option2 (Large-machinery technology)
Percentage of plant cost
Option 3 (Non-recovery & Large-machinery)
Percentage of plant cost
Unit: Million Renminbi per year
Cost
Cost
Cost
6206
96.2%
24673
244
3.8%
8573
6
0.1%
4
74.21%
25.79%
0.01%
18512
4730
5
79.63%
20.35%
6456
100%
3325C
100%
2324
0.02% 100%
Source: Author
Figure 6.3: Plant-Cost Comparison of Three Coke-oven Technology Options
Plant-Cost Comparison of Three Coke-oven technology Options
30000
U Operational Cost
* Investment Cost
3 Land Cost
25000
20000
E
e
15000
10000
5000
Options
0
Option 1
Option2
Option 3
Source: Author
The operational cost has the greatest impact on the plant cost (Figure 6.3).
The Option 1 has the highest percentage of operational cost in the total plant
cost, because the Option 1 is more labor-intensive compared with the other two
options. The investment cost is 4% of the total plant cost in Option 1. This
percentage increases to 26% in Option 2. In Option 1,the non-recovery
technology does not recover the by-products, and plants do not purchase and
install the equipment and facilities for by-product recovery and pollution
abatement. By contrast, in Option 2, the large-machinery technology requires
more initial investment inputs for the advanced machinery equipments and
facilities for by-product recovery and pollution abatement. But recently, the
cokemaking industry has shown the trend to use both two technologies together.
Some non-recovery cokemaking have changed to more mechanical processes,
such as using transferring belts in coal loading and unloading. Some largemachinery plants near Linfen are trying another simplified large-machinery
technology, which does not recover by-products and does not need equipment
and facilities installed for by-product recovery and pollution abatement (AGS
MRP Field Trip Notes, 2002). This trend can take advantage of the two
technologies and achieve the production improvements by reducing cost,
improving energy efficiency, and abating pollution.
6.1.4 New Highway Construction and Speed Improvement Impact Analysis
The highway system has developed quickly in Shanxi Province. I
obtained the information presented here concerning future highway plans from
the interview with one official in the Shanxi Province Transportation Planning
Department (AGS MRP Field Trip Interviews, 2002). With Taiyuan as the
transportation hub, the highways in Shanxi Province form a road network linking
all the counties in the province (Figure B.3). Inthe north-south direction, the
Datong-Yuncheng Highway is a major highway connecting the cities in south and
north of the province. The new Taiyuan-Changzhi-Jincheng Highway will be built
from 2005 to 2008. Inthe west-east direction, the Taiyuan-Jiuguan Expressway,
which joins the Beijing -Shijiazhuang expressway, connects Beijing-TianjinTanggu expressway and Beijing-Shenzhen expressway, and leads to Beijing and
the region of Bohai Sea rim directly. This highway will extend westwards from
Taiyuan to Lishi-Liulin area when several new highways are built in the next five
years. Inthe Tenth Five-Year Plan, Shanxi Province will improve its highway
transportation system and plans to invest at least RMB 10 billion on new highway
construction in the next five years (China Tenth Five-Year Plan, 2001). During
the interview, the official inthe Shanxi Transportation Planning Department told
me that besides the new highway construction, another great change recently is
that road conditions have been improved, especially the roads in the towns and
villages. The average speed limit on the roads in towns and villages has
increased from 30 kilometers per hour in 1990 to 40 kilometers per hour today.
Based on the map of Shanxi Province major highway construction in the
tenth five-year plan provided by Shanxi Province Development and Planning
Committee, I added two major new highways inthe SPGPSS, TaiyuanChangzhi-Jincheng Highway and Taiyuan-Lishi-Liulin Highway and increased the
speeds of the town-village level roads from 30 kilometers per hour to 40
kilometers per hour. This speed improvement can increase fuel efficiency from
0.056 liter/km-tonne to 0.043 liter/km-tonne for transportation on those roads
(Akatsuka, 2001). After running the SPGPSS inthe different scenarios, I
obtained the results in Table 6.4.
Table 6.4: Comparisons Before and After the New Highway Construction and
Road-speed Improvements
Transportation Cost (Million Renminbi/year)
Scenarios
2000 Base
2000 Transport-Min
2000 Plant-Min
Before
1104
After Decrease Percentage
2.0%
1082
574
556
3.1%
1341
1322
1.4%
Transportation PM Emissions (kilogram/year)
Scenarios
Before
After Decrease Percentage
2000 Base
1,622,440
1,596,488
1.6%
2000 Transport-Min
2000 Plant-Min
Source: Author
802,219
2,181,582
788,258
2,154,0161
1.7%
1.3%
The transportation cost and pollution emissions are all decreasing after
the new highway construction and road-speed improvements. Inthe three
scenarios, the Transport-Min Scenario has the biggest decrease and the PlantMin Scenario has the least decrease both for transportation cost and emission
pollutions, because the Transport-Min Scenario can more effectively take
advantage of new highway construction and road-speed improvements to reduce
the cost and emissions.
6.1.5 Industrial-Park Location-Choice Analysis
Inthe future provincial planning, Shanxi government officials plan to build
two cokemaking industrial parks in the province to increase economies of scale
and reduce environmental pollution. They have tentatively selected locations in
the Lishi-Liulin, Linfen, and/or Jiexiu areas. These three places are currently the
major cokemaking industrial areas in Shanxi Province. By using the SPGPSS, I
make simulations of the several location arrangements of those industrial parks
and compare the transportation cost, energy consumption, and emissions from
them.
As noted in Section 4.2, I assume three combinations of cokemaking
industrial park locations: (1) Lishi-Liulin and Linfen, (2)Linfen and Jiexiu, and (3)
Lishi-Liulin and Jiexiu. Inthese three scenarios, I assume that each industrial
park produces half of the current total coke production of Shanxi Province. I
show the SPGPSS results in Table B.6 and maps in Figure B.4.
When I compare the three combinations of cokemaking industrial parks,
the Linfen-Jiexiu scenario has the lowest cost, energy consumption, PM
emission, and SOx emissions from coal and coke transportation. Based on this
simulation analysis by the SPGPSS, I would recommend Shanxi government
choose to build two cokemaking industrial parks in the Linfen and Jiexiu areas.
From the three maps of each industrial park combination, I find that in the Linfen
and Jiexiu scenario, the industrial parks locate nearer to the big coal suppliers
and major highways and railways than in the other two scenarios, which
consequently reduces the transportation cost, energy consumption, and emission
pollution. Of course, additional factors should be taken into consideration for the
industrial-park location decision, but I have used this to illustrate the type of
quantitative comparisons policy makers and plant managers can conduct by
making these types of simulations with the SPGPSS.
6.2 Plant Case Studies
At the plant level, I did a typical plant case study of multi-plan valuations
and comparisons in terms of location choice, transport routes and modes and
coke-oven technologies. For confidential reasons, I call this plant X cokemaking
plant.
6.2.1 Choose transport routes and modes
Taking advantage of the friendly GUI in the SPGPSS, coke managers can
choose the coal and coke transport routes for their companies. After they select
the railways and roads they want to use for the company's transportation, those
transport routes will be highlighted. Then, by using the statistical tool embedded
in the SPGPSS, coke managers can get the total cost, energy consumption, and
emissions for transporting one tonne of coke.
I give an example of how to use the SPGPSS in the analysis of the coke
transportation routes for X cokemaking plant. First, from the 2000 AGS MRP
WE Survey, I find that all the coke X plant produced in year 2000 is exported to
the United of States, Germany, Japan, and South Africa. The coke
transportation mode can be by truck or by railway. From the 2000 field-trip
interview, I learned that X plant currently transports almost all the coke by train to
Tianjin Port, from where it is shipped overseas. The major railway they use in
Shanxi Province is the Shijiazhuang-Taijiu Line, as highlighted in Figure 6.4.
Figure 6.4: Coke Transportation Choices of X Cokemaking Company
By Highway
By Railway
Source: Author
Due to the limited railway capacity, the continuing production growth of X
plant, and the rapid development of the highway system, I consider the
alternative of transporting X plant's coke by truck on the highways. If all the coke
of X plant is transported by the roads (highways are highlighted in Figure 6.4),
the corresponding cost, energy consumption, and emissions for transporting one
tonne of coke by railway or by truck are shown in Table B.7. For the coke
transportation of X cokemaking plant, railway is a much better choice than truck,
especially from the aspects of energy consumption and pollution emissions
(Table B.7). Currently, the cost of railway transportation is less than road
transportation, but the increasing price of railway transportation and the limited
capacity could restrict the use of railway transportation to some extent. Because
of using heavy diesel trucks for road transportation, the energy consumption in
the all-by-truck case is about 10 times that in the all-by-train case, the PM
emissions is about 6 times, and the SOx emissions is about twice. By contrast,
the coke export by railway can effectively reduce the pollution emissions from
transportation.
6.2.2
Selection of a new location
Inthe location-choice analysis, the manager of the X cokemaking plant
plans to move the plant to a new location or expand the plant capacities in place
nearer to suppliers. I assume the plant would not change its suppliers
(coalmines) and coke consumers. From the 2000 AGS MRP TVE survey, I find
that the X cokemaking plant has four major coal suppliers, two of them are
located 30-40 kilometers away from the current plant, and another two are
located 80-100km away from the current plant. The plant transports all of their
coal from the suppliers by truck. From the comparison of the old location
scenario (Table B.8) and new location scenario (Table B.9), I determine that by
relocating the plant to the place of one supplier, which stays nearest to the center
of the road system (Figure 6.5), the cost, energy consumption, and pollution
emissions of the X plant's coal transportation all decrease. The SPGPSS system
efficiently gives the evaluations of transportation cost and other measures
according to the locational choices of the users. To compare those results, users
can decide which is the best locational choice from the transportation
perspective.
Figure 6.5: Locational Choice of X Cokemaking Plant
Source: Author
6.2.3 Choose coke-oven technology
I also can estimate the different plant cost with the different adoption of
coke-oven technologies (Table B.10). Currently, X cokemaking plant is using
non-recovery technology. In 2000, the plant scale was 600,000 tonnes. The
plant production was 500,000 tonnes (AGS MRP TVE 2000 Survey).
By using the plant-cost formulae, I estimate the operational cost,
investment cost, and land cost with the adoption of large-machinery coke-oven
technology. I find that the adoption of the large-machinery technology is much
more expensive than the non-recovery technology, although non-recovery
technology consumes more land and has a higher land cost than the largemachinery technology.
Using the SPGPSS, I conduct two levels of research with the different
data requirements. Based on the results of alternative analyses on the
provincial level, I optimized the plant locations and transport flows in terms of the
total cost, energy consumption and pollution emissions, and valued the impacts
from coke-oven technologies, new highway construction and speed
improvements, and industrial park establishments. The SPGPSS also can help
coke managers on the choices of individual plant location and transportation
issues.
Chapter 7
CONCLUSION
I created a Shanxi Province GIS-based Planning Support System
(SPGPSS) for the transportation and industrial plant location studies of the
cokemaking sector in Shanxi Province. By integrating database, map viewer,
scripts, and professional models in the GIS environment, the SPGPSS is able to
optimize plant locations, transport routes and modes under the different
scenarios on the provincial level, and also compute the corresponding cost,
energy consumption, and pollution emissions in the transportation process.
Policy makers and industrial organizations can utilize the SPGPSS to value the
economic and environmental impacts from different policy possibilities and assist
their planning decisions on location rearrangements and structural changes. On
the plant level, I used the GIS functions and tools to conduct spatial analyses and
evaluations for an individual plant in the planning of transport routes and new
location. The coke managers can compare different transportation and location
plans for an individual plant and choose the best plan according to their
requirements. By simulating alternatives and further comparative analyses in the
GIS environment, the SPGPSS is capable of assisting and supporting real-time
decision-making in the planning process.
By the applications of SPGPSS, I tested my hypothesis that combining
plants into several large-capacity plants or industrial parks is preferable to having
them distributed throughout the region. From the perspective of total cost
minimization, the merged large-capacity cokemaking plants and industrial parks
instead of the distributed small-capacity plants would reduce the total cost from
the transportation and cokemaking process. From the perspective of total energy
consumption and pollution emission minimization, however, the merger of
dispersed small-capacity plants to large-capacity plants and industrial parks
would increase the total energy consumption and pollution emissions. These
conclusions are based on the assumptions and model optimizations I used in this
study.
I also found that the type of coke-oven technology used has a great
impact on the plant cost, energy consumption, and pollution emissions, which,
consequently, can affect the plant location and transportation choices a plant
manager makes. The transportation cost and pollution emissions both
decreased after new highway construction and road-speed improvements were
completed, especially in the transport-minimization scenario. Inthe industrialpark location analysis, the simulation results indicate that the choice of the Linfen
and Jiexiu cokemaking parks would reduce transportation cost, energy
consumption and pollutions more than the other two alternatives due to their
closer access to big coal suppliers and major highways and railways. I also used
the SPGPSS to assist managers at individual plants make decisions on their
choices of location, transport, and coke-oven technology based on the detailed
plant information from the surveys and interviews.
APPENDIX A: Transportation Cost, Energy Consumption, and NOx
Emissions for Each Transportation Link in the GIS system
Kraines and Akatsuka in Tokyo University first started the transportation
studies in Shanxi Province. They inserted into the GIS maps the locations of
major coalmines, cokemaking plants, and coke consumers in Shanxi Province
and the production, consumption, and export data as well as the capacities of
major railways from the 1990 Energy Resources Atlas of Shanxi Province
(Shanxi Committee of Atlas Compilation, 1994). They also developed the
formulae to calculate the transportation cost, energy consumption, and NOx
emissions for each transportation link in the GIS system (Kraines et al., 2001).
Table A.1: Transportation-cost Coefficients for Diesel Trucks
Item
Diesel Truck
Units
Reference
unit capacity
10
tonnes
(Survey Team 1999)
weight factor ()
0.5
-
(Akatsuka 2001)
operator wages (Cp)
0.75
RMB/tonne-hr
(Kraines and Akatsuka 1999)
fuel efficiency (Eff)
0.025 - 0.055
liter/tonne-km
(Kraines and Akatsuka 1999)
fuel cost (Cf)
2.7
RMB/liter
(Kraines and Akatsuka 1999)
vehicle cost
200,000
RMB
(Kraines and Akatsuka 1999)
vehicle lifetime
10
year
investment cost (in)
0.05
RMB/tonne-km
(Kraines and Akatsuka 1999)
(Shanxi Province Statistics Office 1999b)
loading cost
0.5
RMB/tonne
(Akatsuka 2001)
Table A.2: Transportation Cost Coefficients for Diesel and Electric Trains
Reference
Item
Diesel
Electric Units
unit capacity
2300
3000
tonnes
(Shanxi Province Statistics Office 1999a)
fuel efficiency (Eff)
0.005
0.0113
liter/tonne-km
contracted cost (Ct)
0.11
0.11
RMB/tonne-km
(Shanxi Province Statistics Office 1999a)
(Kraines and Akatsuka 1999)
Table A.3: Transportation Energy Consumption Coefficients for Diesel Trucks and
Trains
Item
Diesel Truck
weight factor (j
0.5
fuel efficiency (Eff)
0.025 - 0.055
diesel heat value(Hd) 9.2
Diesel Train
Units
Reference
(Akatsuka 2001)
0.005
9.2
liter/tonne-km
(Kraines and Akatsuka 1999)
Mcal/liter
(Kraines and Akatsuka 1999)
Table A.4: Transportation Energy Consumption Coefficients for Electric Trains
Item
Electric Train
Units
Reference
power efficiency of electric trains (Eei)
0.0113
kWh/tonne-km
(Shanxi Province
Statistics Officel 999a)
power efficiency of power plants (Pf)
0.30
-
(Sadakata 2000)
Table A.5: Transportation NOx Emission Coefficients for Diesel trucks, )iesel Trains,
and Electric Trains
Value Units
Item
8.0
NOx emission per unit engine power (Noe)
0.15
engine power per heat value of diesel (Ef,)
9.2
heat value of diesel fuel (Hd)
Reference
g/kWh
(Faiz et al. 1996)
-
(Global Network 2001)
Mcal/liter
(Transport ation Ministry of Japan
1999)
0.976
NOx emission of power plants (Nop)
power efficiency of electric trains (Eei)
0.0113
power efficiency of power plants (Pf)
g/Mcal
(Bernstein et al. 1999)
kWh/tonne-km (Shanxi Province Statistics Office
1999a)
0.30
(Sadakata 2000)
Formulae to calculate the transportation cost, energy consumption, and
NOx emissions
Truck transport cost [RMB / tonne] = Cf * Eff* di (1+ _) + C, * di / v + In* di
Train transport cost [RMB / tonne] = Ct* di
Diesel Truck and Train energy consumption [Mcal/ton] = Eff(v) VHd V d V(1+)
Electric Train energy consumption [Mcal/ton] = Efe Vdi Pf
Diesel train and truck transport NOx [g NOx / tonne] = Noe * Efc * Ef* Hd * d,
Electric train transport NOx [g NOx / tonne] = No, * Efe * di I Pf
APPENDIX B: Application Results
Table B.1: Road Transportation vs. Rail Transportation (2000 Base Scenario)
Transport Flow
Transport Cost
Percentage
Percentage
78%
75%
Road Transport
15%
60%
22%
56%
25%
22%
Rail Transport - Coal
6%
5%
Rail Transport - Coke
19%
17%
Road Transport - Coal
Road Transport - Coke
Rail Transport
Source: Author
Table B.2: Coal Transportation vs. Coke Transportation (2000 Base Scenario)
Coal Transport
Coal Transport-Road
Coal Transport -Rail
Coke Transport
Coke Transport-Road
Coke Transport - Rail
Transport Flow
Percentage
20%
15%
5%
80%
61%
Transport Cost
Percentage
27%
22%
5%
83%
56%
19%
17%
Source: Author
Table B.3: PM Emissions from Transportation and Cokemaking Plants, 2000
2000Transport-Min 2000Plant-Min
2000Base
Scenarios
Large-machinery technology
2,181,582
802,219
1,622,440
From Transportation
83,237,500
83,237,500
83,237,500
From Plant
85,419,082
84,039,719
84,859,940
Total PM
Non-recovery technology
2,181,582
802,219
1,622,440
From Transportation
33,295,000
33,295,000
33,295,000
From Plant
35,476,582
34,097,219
34,917,440
Total PM
Unit: kilogram/year
PM = particulate matter
Source: Author
Table B.4: SOx Emissions from Transportation and Cokemakin Plants, 2000
2000Plant-Min
2000Base
2000Transport-Min
Scenarios
Large-machinery technology
597,763
1,514,819
From Transportation
1,198,883
From Plant
13,318,000
13,318,000
13,318,000
13,915,763
14,832,819
14,516,883
Total SOx
Non-recovery technology
_
_
1,514,819
597,763
1,198,883
From Transportation
59,931,000
59,931,000
59,931,000
From Plant
61,445,819
60,528,763
61,129,883
Total SOx
Unit: kilogram/year
Source: Author
Table B.5: Transportation Energy Consumption, 2000
Scenarios
Transportation
Energy Consumption
Unit: 1000 kcal/year
Source: Author
2000 Base
2000 Transport-Min
2000Plant-Min
792,347,689
370,842,199
1,058,481,736
Table B.6: Comparison of Three Cokemaking Industrial Park Scenarios
Lishi-Liulin & Jiexiu Linfen & Jiexiu
Lishi-Liulin & Linfen
Scenarios
2,160,621,076 1,883,268,865
1,885,228,826
Cost (RMB/year)
2,079,814,965 1,773,941,756
1,921,549,427
Energy (1000kcal/year)
3,372,174
3,995,512
3,492,874
PM (kg/year)
2,208,906
2,552,780
2,252,170
Sox (kg/year)
Source: Author
Table B.7: Coke Transportation of X Cokemaking Plant
PM
Energy
Cost
(RMB/1000-tonnes)
All by train
All by truck
Source: Author
26387
72973
(kcal/tonne)
9384
97544
SOx
(g/tonne)
(g/tonne)
33
191
50
112
Table B.8: Locational Choice of X Plant: Old-location Scenario
Supplier 1
Supplier 2
Supplier 3
Supplier 4
Total
Source: Author
Cost
Energy
PM
SOx
(RMB/1000-tonnes)
(kcal/tonne)
(g/tonne)
(g/tonne)
10311
9569
18630
14715
53225
11339
11606
22720
20096
65761
23
23
45
38
129
15
15
31
25
86
Table B.9: Locational Choice of X Plant: New-location Scenario
PM
Energy
Cost
(g/tonne)
(kcal/tonne)
(RMB/1000-tonnes)
0
0
0
Supplier 1
14
7360
6069
Supplier 2
36
18474
15130
Supplier 3
58
29149
21733
Supplier 4
108
54983
42932
Total
Source: Author
SOx
(g/tonne)
0
4
25
35
64
Table B.10: Plant-cost Comparison of Different Coke-oven Technologies
Large-machinery coke-oven
Non-recovery coke-oven
technology
technology
332,292,000
85,700,000
Operational cost
90,105,000
2,565,000
Investment cost
45,300
66,000
Land cost
422,442,300
88,331,000
Plant cost
Unit: Renminbi/year
Source: Author
Figure B.1: 2000 Plant-Min Scenario for Total Cost Minimization
Legends
(1000 tonnes/year)
Cokemaking Plant
03000
Coal Mines
A0 - 385
A 386-1358
g 359 -3000
A 3001 - 6058
Coke Users
* 0-252
E253 -979
9980-2798
279-116895
Coal Road Transport Flow
-500
N
500 - 1000
000 - 2000
000 - 3000
000O- 15590
Coal Rail Transport Flow
A1 -500
00-1000
000-2000
000-3000
00-18857
Coke Road Transport Flow
\/1-500
00-1000
000 - 2000
000-3000
000 - 15590
Coke Rail Transport Flow
i/-500
500 -1000
N
000 - 2000
000 -3000
E000W.-I...;i
18857
=Towns
S
Source: Author
"
Shanxi-border
-vmnviwp-w
Figure B.2: Particulate Emissions in the 2000 Transport-Min Scenario
Legends
(1000 tonnes/year)
Cokemaking Plant
03000
Coal Mines
-A 0-385
A 386 -135 8
A 1359 -30 00
A 3001 - 6058
Coke Users
S0 -252
S253 -979
S380 - 2798
2789 - 11695
Coal Road Transport Flow
/-500
400 -1000
"/000- 2000
S000 - 3000
000 - 15590
Coal Rail Transport Flow
/\/1-500
/500 - 1000
"t000 - 2000
000-3000
000 - 18857
Coke Road Transport Flow
b\/1-500
500 - 1000
"000
- 2000
%W000
000 - 3000
-15590
Coke Rail Transport Flow
i - 500
NV500
W
~
E
N
Source: Author
-
1000
"/000 -2000
ANW000 -3000
-16857
000O
Towns
Shanxi border
Figure B.3: Highway System in Shanxi Province
N
E
W+
S
Source: Author
Figure BA: Different Industrial-Park Locations in the PM Emission Minimization
Linfen and Jiexiu Scenario
Legends
(1000 tonnes/year)
1 Cokemaking Industrial Parks
Coal Mines
A 0 -385
A 386 - 135 8
A 135 - 3000
A
Fe'
3001 - 6058
Coke Users
* 0-252
M253-979
980-2798
2798-11685
Coal Road Transport Flow
1-500
000-2000
000 - 3000
M - 15580
Jiexiu
Coal Rail Transport Flow
/V 00 --1000
00
"000
- 3000
000 -16857
Coke Road Transport Flow
- 500
-1000
00-3000
- 15590
Coke Rai Transport Flow
/ i- 500
000- 000
Linfen
-3000
-is165
PETowns
//Shnxiborder
Lishi-Liulin and Jiexiu Scenario
Lishi-Liulin and Linfen Scenario
-Lishi-Liulin
Linfen
"NJiexiu
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