About IPAC Model

About IPAC

01
Figure 1 Model Structure of IPAC

 

In order to quantify GHG emissions from various sources, a modeling framework was developed to analyze possible pathways of energy activities and GHG emissions for the world and China. Structure of IPAC is displayed in Figure 1. IPAC is a multi-model framework, which cover different modeling methodologies by focusing on various policy questions(see figure 2). These modules in IPAC are currently soft-linked, which means the output of one module is used as the input of another module. In IPAC-Global model, the modules have realized the hard link.

 

Now IPAC including global model, national model and regional models. There are two global models in IPAC, one is IPAC-TIMER model which originally come from IMAGE model, and the other one is IPAC-Global model, which was developed in the IPCC SRES scenario development process, and it will be used in this RoSE study. National models covers CGE model, technology simulation model. The provincial model, or regional model is using same model with national one of technology simulation, which cover all provinces in China. And recently, in order to support low carbon development in cities, city level technology models were developed.

02

Figure 2 Policy questions and linkage with modeling

 

IPAC models

IPAC-Emission
IPAC-SGM
IPAC-AIM/Technology
IPAC-AIM/Local
IPAC/Gains-Asia
IPAC-LEAP


IPAC-Global Model

General introduction

 

IPAC-Global model is an extended version of the AIM-Linkage model used in IPCC Special Report on Emission Scenarios (SRES). This model links the social and economy development, energy activities and land use activities, and forms a full range of emission analysis.  IPAC includes mainly four parts: (1) society, economy and energy activities module, which mainly analyzes the demand and supply in the condition of social and economic development, and determines the energy prices; (2) energy technology module, which analyzes the short and mid-term energy utilization technologies under different conditions, and determines the energy demand under different technology compositions. The energy demand in energy technology module will modify the short and mid-term energy demand in society, economy and energy activities module, which makes the energy analysis in macro-economic model better reflect the short and mid-term energy activities; (3) land use module, which analyzes the emissions from land use process. This mainly includes emissions from agricultural food supplies, stock raising, forest management and biomass energy production; (4) industrial process emission module, which mainly analyzes the emissions from all kinds of industrial productions. The society, economy and energy activities module is built based on ERB model developed by Pacific Northwest National Laboratory (PNNL) in US. Energy technology module is the IPAC-AIM/technology module developed collaboratively by Climate Change Strategies Assessment Research Team in ERI and National Institute of Environmental Studies in Japan. Land use module is modified and extended based on the AGLU model developed by PNNL. IPAC-Global module is a global model which includes 9 regions: US, West Europe and Canada, Asia Pacific OECD countries, Economies in Transition Countries, Chian, Middle East, Other Asian Developing Countries, Africa, and Latin America. These regions can be altered; and when being altered, the corresponding data should also be modified. IPAC model can project to 2100. The previous 50 years have a more detailed analysis, with the time interval of 5 years; the latter 50 years have the time interval of 25 years.

Model description

model structure

 

IPAC-Global model links several models to calibrate the data and perform scenario quantification. Major emission sources including energy activities, industries, land use, agriculture, and forests can be simulated in the model framework. The structure of the IPAC-Global linkage model is shown in Figure 3.
GM01

Figure 3 Outline of the IPAC-Global model

The components of the model framework were adopted from previous studies. The energy sector top-down module was developed based on the Edmonds-Reilly-Barns (ERB) model (Edmonds et al., 1983; Edmonds et al., 1995; Edmonds et al., 1996), which is widely used for emission analysis; the end-use module was taken from the AIM/end-use model (AIM Project Team, 1996; Hibino et al., 1996); and the land-use module was developed from the Global Trade Analysis Project (GTAP) model (Hertel, 1997). This new model structure maximizes the ability to simulate a variety of inputs at a variety of levels, incorporating the strengths of both top-down and bottom-up approaches. A bottom-up model reproduces highly detailed processes of technology development related to energy supply and demand, in order to determine future improvement of end-use efficiency. A top-down model, on the other hand, estimates equilibrium of energy supply and demand, and then determines energy prices that reflect not only energy service demand, but also energy efficiency improvement.

The IPAC-AIM/technology model was developed based on AIM/end use model, which is part of the Asian-Pacific Integrated Model (AIM), which was developed by the National Institute for Environmental Studies (NIES) and Kyoto University. It is a bottom-up, energy-technology model. Based on detailed descriptions of energy services and technologies, it calculates the total energy consumption and production in a bottom-up manner. This model has been used to analyze several key countries in the Asian region including China, India, Indonesia, and Japan etc. The AIM/end-use models for key Asian developing countries have been constructed, and the results of analyses using this model have been reported (Jiang et al., 1998; Hu et al., 1996). Among the advantages of bottom-up models, the most important is that their results can be interpreted clearly because they are based on detailed descriptions of changes in human activities and technologies.

The top-down model for the energy sector provides a consistent, conditional representation of economic, demographic, technical, and policy factors as they affect energy use and production. It is a macroeconomic partial-equilibrium model that deals with energy activities and forecasts energy demand over the long term. It uses gross domestic product (GDP) and population as future development drivers, combined with other energy-related parameters to forecast energy demand based on the supply and demand balance. Three end-use sectors -- industrial, residential and transportation -- and one energy-conversion sector -- power generation sector -- are specified in the model. Energy efficiency is described by both technology efficiency and social efficiency improvements. A number of technologies in these four sectors are listed in the model to present different possibilities of technological progress. A link between the bottom-up energy model and the top-down energy model has been developed. A detailed energy-use analysis for the developing Asia-Pacific region from the bottom-up model drives the energy-use pathway before 2030, while a simplified linkage is presented for other regions in the model. The linked AIM/end-use model and the energy top-down model are composed of the energy model in the model framework.

The IPAC-Global model combines these various components to calculate future GHG emissions in a relatively full-range analysis. For the purpose of the model, the world is divided into nine regions: USA, Western Europe OECD and Canada, Pacific OECD, Eastern Europe and the former Soviet Union, Central Planned Asia and China, South and East Asia, Middle East, Africa, and Central and South America. The model has a time horizon extending from 1990 to 2100. The time steps are in units of 5 years up to 2030, followed by time steps at 2050, 2075, and 2100. The GHGs covered in the nonintervention emission scenarios are CO2, N2O, CO, NOx, and CH4. Because SO2 has strong influence on climate change and is important pollutant in local area (Gan, 1998; Qi et al, 1995), it is also included. CO2 emissions are analyzed in the intervention scenarios.

2.2 Key calculation process

Regarding Figure 3, the GHG emissions from energy consumption and energy production are simulated by the energy model. GHG emissions from land use are derived from the land-use model, while GHGs from other emission sources are calculated by simplified industry process models that describe the relationship between GDP per capita and industrial product outputs.

The energy top-down module is a partial-equilibrium model. Energy demand is determined based on socioeconomic development and energy prices. International trade and environmental taxes are introduced for the energy market. The price of primary energy is calculated based on the production cost at the output site, the transportation cost, and tariffs and related taxes levied on the fuel, according to the following formula:

                  GM02                                           

where Pi,t is the energy price at production site, tfi,j,t is the tariff rate, tri,t is the transport cost, and tx1i,j.t and tx2i,j.t are the proportional and additional taxes, respectively. The model allows the trade of three aggregate energy carriers: liquids, gases, and solids.

The calculation for energy service is based on the population, GDP growth and energy price.

                  GM03      for residential and commercial sector
GM04             for industry

where FK is the demand for energy service, BES is the energy service in base year, Pk,l,t  is the energy Service price in sector l at time t using fuel k. rpkl  is the price elasticity,  YLM is the GDP index, ryk is the income elasticity, ZLMtis the population growth index.

The share of energy service supplied by fuel depends on the fuel price.
.
GM05                       

where ai,j,k is a scale constant for fuel i, Rijt is the price of fuel i,  and rj,k is a Logit parameter.

The fuel used to supply the energy service relies on the end use efficiency improvement. The model divides efficiency into two parts: energy end use technology efficiency and social efficiency. Here, social efficiency reflects changes in the production structure or in the energy service structure within the sector.

The amount of exploited energy depends on the energy price and exploitation cost. In the model, the energy resource is graded by the exploitation cost. The exploitation cost is determined by the progress of exploitation technology. The model has two classes of energy supply technologies. The first class, which includes conventional oil, conventional gas, and hydroelectricity, is resource constrained. That is, the amount of the resource available is sufficiently small, relative to the potential demand for the resource, that the level of production is constrained by available resources. The second class, which includes unconventional oil, unconventional gas, coal, nuclear, and solar energy, is considered resource unconstrained. That is, the amount of the resource, relative to the potential demand, is sufficiently large that for practical purposes, resource size alone does not constrain the rate of production.

The model seeks the balance between energy demand and energy supply to obtain an equilibrium price. After the balance is obtained, the energy demand is fixed. GHG emissions are calculated from the energy used in the end-use technologies (Figure 4).
GM06

Figure 4 Outline of ERB model.


 

IPAC-AIM/Technology Model

The AIM/end-use model is composed of 3 modules, as shown in Figure 1. The first is an energy service estimate module that estimates various demands that will need to be met by using energy (energy service). This module obtains its forward linkage from other models and scenarios that decide socio-economic variables. It estimates energy service demand using a basic unit that reflects lifestyles and the concept of environmental conservation. The second module is an energy efficiency estimate module that calculates the improvements in energy efficiency. It comprises “the Reference Energy System (RES)’ which connects the energy supply from the secondary energy step and energy service demands and links them with technological information about energy use tools. The third module selects various service technologies that define energy efficiency. It evaluates the benefit of service tools with standards such as economic efficiency and selects the optimal tools for each situation and service. Also included is a module that estimates the optimal solutions for each sector by combining these 3 modules. Their functions are modulized and designed to treat all time periods, all countries and all sectors with a single sub-program and to link them with other models of AIM through the energy macroeconomics linkage.
AIM01

 

Figure 1. Structure of IPAC-AIM/technology model

 

 

The IPAC-AIM/technology model estimates energy consumption and CO2 emission in the following ways. It:

(1) Estimate the amount of energy service (the amount of production, traffic volumes and air conditioning demand, etc.) by scenarios and models; and,
(2) Select service production technologies to meet this amount of service. At this time, more economical technologies replace and/or supplement older technologies in all level. Then it,
(3) Calculates the amount of energy needed to operate these technologies; and,
(4) Within the amount calculated in (3), as for electricity and steam, selects more efficient energy conversion technologies for secondary energy production and calculates the amount of energy needed to operate that technology; and, if there is any wasted energy during one production process, technologies for energy recovery will be selected to satisfy some energy demand in the sector. Finally,
(5) Using the energy consumption by fuel type calculated above, CO2 emission is derived.

The most important part in this flow is the procedure to select service production technologies. Several constraints are considered in the selection. For example, service devices should supply sufficient energy service to meet the demand of consumers. There are, however, limitations on energy resources and available service technologies. Under these constraints, technology selection is made based on different technology situation, and the criteria for these selections are described as following.

Case 1: The technology is to be replaced.

If a current technology has reached its schedule replacement time, a decision is needed whether to introduce an older technology to supply the service demand or a more expensive energy conserving technology. Thus, taking into account both the difference of purchase prices and the cost of fuel that can be saved leads to choosing more economical technology. Also, where current tools are not sufficient and new tools are needed because of an increase in demand, the decision whether to introduce previous tools or energy conservation tools are made in the same way.

If  (FA+EA)<(FB+EB)®Select Technology A
If  (FA+EA)³(FB+EB)®Select Technology B
where F : Fixed annual cost  E : Annual fuel cost
A : Technology A (previous tool)
B : Technology B (energy conservation tool)

Case 2: Technology has not yet reached their replacement time

The method for selecting technologies in this stage depends on the types of substitutive technologies available, i.e., whether they are:

  • Technologies of a different kind (equipment that need to be completely replaced), or
  • Technologies in a different stage (equipment that need to be partly replaced)

If a current technology is to be replaced at a particular time, it is replaced and/or upgraded only when the total cost of replacement and improvement is less than the cost of fuel saved by energy conservation.

i) Technologies of a different kind

A comparison is made between the fuel cost of the current technology and the sum of the replacement cost and the fuel cost of the substitutive technology, and the current technology will be totally replaced if the substitutive technology is more economic.

If  EA£(FB+EB)®Current technology is not replaced
If  EA>(FB+EB)® Current technology is replaced by the new technology
where F : Fixed annual cost  E : Annual fuel cost
A : Technology A (current equipment)
B : Technology B (substitutive equipment)

ii) Technologies at different stage

A comparison is made of the fuel cost of the current technology and the total increased cost for upgrading current equipment (improvement cost) and the fuel cost. The current technology will be partly replaced if the upgrading technology is more economic.
Even the technology is improved, the improvement will not lengthen the assumed life span of the current technology.

If  EA£(DFB+EB)®Current technology is not improved
If  EA>(DFB+EB)® Current technology is improved
where F : Fixed annual cost  E : Annual fuel cost
A : Technology A (current equipment)
B : Technology B (upgraded equipment)
DF: Improvement cost

Because these decision-making processes are included in the model, different technologies will be selected if carbon taxes or subsidies are introduced. As a result, energy consumption and CO2 emission vary. For example, if a carbon tax is introduced, the price of energy will rise and the cost of fuel saved by energy conservation will increase. This will make possible the introduction of comparatively expensive energy conservation technologies. The introduction of subsidies will reduce the purchase price of energy conservation technologies, and this in turn will also promote their introduction.

Two linear program submodules were developed to analyze optimal resolutions for two purposes. One is to determine the best combination of technologies to allow several kinds of service technologies to provide the best cost-effective services. This submodule specially considers the technologies that can supply more than one energy service. For example, some air conditioner can be used both for space cooling and for space heating, another example is co-generation system which can supply both electricity and heat. The other is to find optimal assignment pattern of subsidies in order to minimize the total CO2 emission. This assignment algorithm has the structure of two dimensional optimization problems: government wants to minimize the total CO2 emission and the market wants to minimize the total cost for energy use. This submodule is developed to solve such a complicated optimization problem so called “Stackelberg Problem” and it enables us to estimate the effects of reinvestment of tax revenue into introduction of energy efficient technologies, and also to estimate the least additional cost that would be required at the national level to reduce CO2 emission.

The most important part in this flow is the procedure to select service production technologies. Several constraints are considered in the selection. For example, service devices should supply sufficient energy service to meet the demand of consumers. There are, however, limitations on energy resources and available service technologies.

Decision is made by the two players- government and consumers. The government wants to minimize CO2 emission by using economic instruments such as carbon taxes and subsidies. Consumers want to minimize costs for satisfying their service demand. A solution of the consumers’ linear programming problem depends on parameters which are decided by the government. This end-use problem can be formulated as the following two-stage minimization problem:

 

                  AIM02                      (1.a)
subj. to AIM03                                                                                               (1.b)

                  AIM04                                             (1.c)

                        subj. to  AIM05   I=1, LL, n                                  (1.d)
                                    Ax ³ b                                                                          (1.e)
                                    x ³ 0, z ³ 0, a ³ 0                                                            (1.f)
where
a=(a1, LL,ak)T denotes carbon tax rate
z=(z1,LL, zn)T denotes amount of subsidies for service device determined by the government and zs is an optimal strategy of the government.
b=(b1, LL,bn)T denotes subsidies rates of service devices determined by the government and bs is an optimal strategy of the government.
x=(x1, LL,xn)T denotes the number of service device used  by the consumers and AIM06 is an optimal strategy of consumers when a,z,b are given.
d=(d1, LL,dn)T denotes CO2 emission from a unit service device.
TSgiven Denotes the total allowable budget for the subsidy.
c=(c1, LL,cn)T denotes costs of service devices without a subsidy.
A denotes a m´n coefficient matrix.
b=(b1, LL,bm)T denotes a constraint vector.
k denotes the number of energy resources.
m denotes the number of constraints.
n denotes the number of variables; and
e denotes a small positive number.


In order to address the question given above, a bottom-up type model: IPAC-AIM/technology model is adopted for this study. Data on economic, production output, technology parameters, energy and feedstock price, energy supply etc. were collected to makeup AIM/end use model for China.

IPAC-AIM/technology model could be simulated based on sector or energy use process. IPAC-AIM/technology model for China is developed from AIM/end use model by extending sectors and energy end use processes. According to the present statistics of China's national economy as well as the data availability, energy end users in this study are divided into five sectors, i.e., industrial, agricultural, service, residential and transport sectors. Table 3.1 gives the classification of sectors and theirs lower level division. Sector could be split into several subsectors or products, or service mode. Subsectors are classified in industry sector, and then every subsector includes one or more products. For example, non-ferrous subsector includes number of products such as copper, aluminum, zinc and lead. For transport sector, transport modes by passenger transport and freight transport are given to make detail analysis.  Residential sector is split into urban and rural to match the different development pattern in these two areas. Different technologies for service demand are collected for every subsectors and products. Energy service and technology selection for each sector or product is decided so that energy consumption and CO2 emission can be estimated.

Table A-1 Sectors in AIM/End use of China


Sector

Sub-Sector

Agriculture

 

Industry

Iron and Steel

Copper

Aluminum

Zinc&Lead

Cement

Glass

Brick

Lime

Ammonia

Ethylene

Soda Ash

Caustic Soda

Fertilizer

Calcium

Paper Making

Textile

Casting

Heat Treatment

Forging

Cutting

Other Industry

Energy Conversion

Power Generation

 

Refinery

Transport

 

Commercial Sector

 

Residential Sector

Urban Resident

 

Rural Resident

Because of the limitation on future technology information, the target year in this study for China is designed to be 2010. Another advantage to select the target year is that the China’s government announced the long-term economic perspective plan for 2010.

CO2 is selected for the representative green house gases. The emission from energy use and industrial production are covered here while CO2 emission from other sources such as emission from land use change is not included.

3.3. Input assumptions and simulation cases

Economic development growth is a key driver for future development pattern. It is commonly viewed that China can continue to develop very quickly if there is no big social turbulence. Many studies had been done to forecast future economic development in China. The economic development from the national long-term development perspective announced by government in China was adopted. From the plan, the annual average GDP growth rates of 9% from 1990 to 2000, and 7.5% from 2000 to 2010 were used as basic economic development scenario in this research.

Population is another key factor to make forecast in this research. Major factors considered for population growth include: planned population policy; fertility reduced with an increase in income; marriage age shifting later; few children by family working patterns of both husband and wife typically having jobs; few children desired by well educated people; average life-span increases; death rate decreases etc. It is believed that the population growth will follow the pattern of developed countries in Asian area. Based on these factors and referred some other forecast results on population, population scenario in this research is: population will be 1.28 billion in 2000 and 1.4 billion in 2010, with a average natural growth rate of 11.4‰ from 1990 to 2000 and 8.8‰ from 2000 to 2010.

Table A-1 lists the major technologies used in this model. In AIM/China, these energy use technologies are mainly broken down into three categories:

  • Technologies for service production: they are the technologies to satisfy service supply. These technologies include renovation for different old technologies and newly installed technologies.

 

  • Technologies of energy recovery utilization: including various technologies of residual heat, combustible gases and black liquor recovery and its utilization.
  • Technologies of energy conversion: in-plant electric power generator, technologies of thermal energy conversion (e.g. industrial boilers) as well as electric power generation using residual heat and combustible gases etc.

 

There are nearly 400 technologies are collected for the analysis, which cover major technologies used in every sector defined in table 3.1. Some advanced technologies are taken into account even though they are not used in China now. The basic data for technologies include purchase price, annual diffusion rate, unit energy consumption, life span, year of entering market and year of obsolescence, production capacity, etc. Technology data was collected from various sources (for example, RCSU, 1994; Shi, 1994 etc.). Data survey was made from industry sectors, government administrations, and relative study projects. A technical report was prepared for the detail information on technologies. The content of this technical report is presented in Appendix E.

The GDP growth rate itself will not be used in the model as input data. The driver for future energy use or CO2 emission is the energy service which can be regarded as the final products or service by consuming energy, such as steel output, space heating area etc. The energy service is given as scenarios under the national economic development. The energy service data is surveyed from the government development plan, sectoral development plan. Energy service from these plan and studies follow the economic development pattern which given by government.

Energy service scenarios for major sectors and products are listed as index in table 3.3. These scenarios reflect the economic development and social structure change in China. The scenarios determine the level of energy service demand increase that will be supplied by selected technologies.
Energy prices were surveyed based on China’s energy market. Future energy prices are given as scenarios when international energy price and domestic energy supply are considered with consulting of experts in China.

In the new run of the IPAC-AIM/technology model of China, special eye was given to nature gas supply. According to the possible future for nature gas production and import, no limitation is given for nature gas use except in residential sector. The nature gas use in residential sector is strongly limited by government policy and investment on infrastructure.

Traditional biomass use was limited to keep at most at the 1994 level in rural resident. The biomass use in rural area is decreasing because of the supply and shifting to commercial energy. The rural resident in low income regions in China tends to user biomass, but the biomass supply in these regions is limited.

To assess effect of technology progress and the alternative policy options on energy consumption and CO2 emission in China, three cases were defined to be simulated in the model. They are shown in table A-2. These cases are selected based on the potential technology progress and energy price system in China, it also should be designed for policies options.

 


Table A-2. Energy service technologies used in this simulation

Classification

Technologies (equipment)

Iron & Steel

Coke oven, Sintering machine, Blast furnace, Open hearth furnace (OH), Basic oxygen furnace (BOF), AC-electric arc furnace, DC-electric arc furnace, Ingot casting machine, Continuous casting machine, Continuous casting machine with rolling machine, steel rolling machine, Continuous steel rolling machine, Equipment of coke dry quenching, Equipment of coke wet quenching, Electric power generated with residue pressure on top of blast furnace (TRT), Equipment of coke oven gas, OH gas and BOF gas recovery, Equipment of co-generation.

Non-ferrous metal

Aluminum production with sintering process, Aluminum production with combination process, Aluminum with Bayer, Electrolytic aluminum with upper-insert cell, Electrolytic aluminum with side-insert cell, crude copper production with flash furnace, crude copper production with electric furnace, Blast furnace, Reverberator furnace, Lead smelting-sintering  in blast furnace, Lead smelting with closed blast furnace, Zinc smelting with wet method, Zinc smelting with vertical pot method.

Building materials

Cement: Mechanized shaft kiln, Ordinary shaft kiln, Wet process kiln, Lepol kiln, Ling dry kiln, Rotary kiln with pro-heater, dry process rotary kiln with pre-calciner, Self-owned electric power generator, Electric power generator with residue heat; Brick & Tile: Hoffman kiln, Tunnel kiln;
Lime: Ordinary shaft kiln, Mechanized shaft kiln; Glass: Floating process, Vertical process, Colburn process, Smelter.

Chemical industry

Equipment of synthetic ammonia production: Converter, Gasification furnace, Gas-making furnace, Synthetic column, Shifting equipment of sulphur removing; Equipment of caustic soda production: Electronic cell with graphite process, Two-stage effects evaporator, Multi-stage effects evaporator, Equipment of rectification, Ion membrane method; Calcium Carbine production: Limestone calciner, Closed carbine furnace, Open carbine furnace, Equipment of residue heat recovery; Soda ash production: Ammonia & salt water preparation, limestone calcining, distillation column, filter; Fertilizer production: Equipment of organic products production, Equipment of residue heat utilization

Petrochemical Industry

Facilities of atmospheric & vacuum distillation, Facilities of rectification, Facilities of catalyzing & cracking, Facilities of cracking with hydrogen adding, Facilities of delayed coking, Facilities of light carbon cracking, Sequential separator, Naphtha cracker, de-ethane separator, diesel cracker, de-propane cracker, facilities of residue heat utilization from ethylene.

Paper-making

Cooker, facilities of distillation, facilities of washing, facilities of bleaching, evaporator, crusher, facilities of de-water, facilities of finishing, facilities of residue heat utilization, facilities of black liquor recovery, Co-generator, Back pressure electric power generator, condensing electric power generator.

Textile

Cotton weaving process, Chemical fiber process, Wool weaving & textile process, Silk process, Printing & dyeing process, Garment making, Air conditioner, Lighting, Facilities of space heating.

Machinery

Ingot process: Cupola, Electric arc furnace, fan; Forging process: coal-fired pre-heater, Gas-fired pre-heater, Oil-fired pre-heater, Steam hammer, Electric-hydraulic hammer, Pressing machine;  Facilities of heat processing: Coal-fired heat processing furnace, Oil-fired heat processing furnace, Gas-fired heat processing furnace, Electric processing furnace; Cutting process: Ordinary cutting, high speed cutting.

Irrigation

Diesel engine, Electric induct motor

Farming works

Tractor, Other agricultural machine

Agricultural products process

Diesel engine, Electric induct motor, processing machine, coal-fired facilities.

Fishery

Diesel engine, Electric induct motor.

Table A-2. Energy service technologies used in this simulation (conti.)

Classification

Technologies (equipment)

Animal husbandry

Diesel engine, Electric induct motor, Other machines.

Space heating in resident

Heat supplying boiler in thermal power plant, Boiler of district heating, Dispersed boiler, Small coal-fired stove, Electric heater, Brick bed linked with stove (Chinese KANG).

Cooling in resident

Air conditioner, Electric fan.

Lighting in resident

Incandescent lamp, Fluorescent lamp, Kerosene lamp.

Cooking & Hot water in resident

Gas burner, bulk coal-fired stove, briquette-fired stove, Kerosene stove, Electric cooker, cow dung-fired stove, firewood-fired stove, methane-fired stove.

Electric Appliance

Television, Cloth washing machine, Refrigerator, others.

Space heating in service sector

Heat supplying boiler in the thermal power plant, Boiler of district heating, dispersed boiler, Electric heater.

Cooling

System of central air conditioner, Air conditioner, Electric fan.

Lighting

Incandescent lamp, fluorescent lamp.

Cooking & Hot water

Gas burner, Electric cooker, Hot water pipeline, Coal-fired stove.

Electric Appliance

Duplicating machine, computer, Elevator, others.

Passenger & freight transport

Railway (passenger & freight): Steam locomotive, Internal combustion engine locomotive, Electric locomotive.; Highway (passenger & freight): Public diesel vehicle, Public gasoline vehicle, Private vehicle, Large diesel freight truck, Large gasoline vehicle, small freight truck.
Waterway (passenger & freight): Ocean-going ship, Coastal ship, Inland ship.
Aviation (passenger & freight): Freight airplane, passenger airplane.

Technology frozen case, only used for comparison with other cases, also can be called as no technology progress case. It is presumed that the present service production technologies and energy efficiency will be remained at the status in 1990 without any technology progress. But it does not mean energy consumption for this case will increase with the same growth rate of economic development.

In market case, an well-operated market is assumed that technologies could be selected based on market mechanism. It will conduct technology options after rationally economic benefits assessments carried out to energy service technologies. This case is designed to emphasize the contribution of market mechanism on energy use conservation and CO2 emission reduction. China is under the stage to reform the economy from planning economy to market economy and it is expected to be finished in next 10 years. Analysis results for this case could be used to explain the benefit of market mechanism, and it is also used as a base line emission scenario for the medium-term.

Policy case is defined to analyze the effects of climatic policies in reducing CO2 emissions. The policy case was defined here as the levying of a carbon tax of 100 yuan per ton carbon and returning all the revenues from this carbon tax as subsidies for advanced technology diffusion.  The introduction of a carbon tax is assumed to begin from 2000. The introduction of advanced technologies would be promoted by the policy to contribute to CO2 emission reduction.

Table A-3.  Energy Service Forecast in Industrial Sector(index, 1990=1)

Energy Service

1990

2000

2010

Steel Output

1

1.8

2.1

Copper output

1

1.5

1.7

Aluminum output

1

2.5

3.5

Synthetic ammonia Output

1

1.3

1.8

Fertilizer Output

1

1.2

1.4

Cement Output

1

2.4

2.9

Brick & Tile Output

1

1.3

1.8

Flat glass Output

1

1.9

3.5

Paper and Pulp Output

1

2.1

2.6

Irrigated Land area

1

1.0

1.1

Cultivated land area

1

1.0

1.0

Residential households in the urban

1

1.5

2.0

Urban housing area of each household

1

1.3

1.6

Changes in cooing intensity in Urban Household

1

1.2

1.4

Changes in space heating intensity in Urban Household

1

1.3

1.3

Changes in cooking and hot water intensity in Urban Household

1

1.4

1.6

Changes in illumination of lighting in Urban Household

1

1.1

1.2

Possessing rate of refrigerator in Urban Household

1

1.5

1.9

Possessing rate of clothes washing machine in Urban Household

1

1.1

1.1

Possessing rate of color TV in Urban Household

1

1.4

1.5

Residential household in the rural

1

1.2

1.2

Rural Resident housing area

1

1.0

0.9

Changes in cooling intensity in Rural Household

1

1.3

1.5

Changes in space heating intensity in Rural Household

1

1.2

1.2

Changes in cooking intensity in Rural Household

1

1.2

1.3

Changes in illumination of lighting in Rural Household

1

1.1

1.2

Possessing rate of refrigerator in Rural Household

1

1.4

4.3

Possessing rate of clothes washing machine in Rural Household

1

1.3

1.5

Possessing rate of TV in Rural Household

1

1.2

1.4

Changes in intensity of space heating in service sector
Changes in intensity of space cooling in service sector
Changes in illumination requirement
Changes in electric power consumed by duplicating machine
Changes in electric power consumed by computer
Changes in electric power consumed by elevator
Changes in electric power consumed by other electric appliance

1
1
1
1
1
1
1

1.2
1.25
1.10
1.10
1.20
1.05
1.10

1.36
1.35
1.20
1.15
1.25
1.10
1.20

Traffic volume of Railway Freight Transport

1

1.5

1.9

Traffic volume of Railway Passenger Transport

1

1.8

2.4

Traffic volume of Aviation(freight transport)

1

11.0

53.1

Traffic volume of Aviation(passenger transport)

1

11.0

52.6

Traffic volume of private car

1

19.8

272.4

Traffic volume of Small truck

1

5.1

9.2

Traffic volume of Large truck

1

1.7

2.9

 

Table A-4 Price and CO2 emission factors

Unit: price:  1990 Yuan, emission factor: g-C/Mcal

 

Emission Factor

Price

 

 

1990

1994

2000

2010

2030

Coal

10062

2.86

2.9

3

3.2

3.5

Coke

12300

8.82

 

9

9.4

9.8

Crude Oil

7811

8

9

10

11

12

Gasoline

7658

25

26

26.5

29

31

Kerosene

7748

25

26

26.5

29

31

Diesel

7839

25

26

26.5

29

31

Fuel Oil

8180

8

8.8

9.4

11

13

LPG

6833

4.29

10

14

16

18

Natural Gas

5639

4.29

10

12

13

16

Town Gas

5835

4

6

8

8.6

9.6

SOLAR Energy

0

 

 

 

 

 

Black Liquid

10751

 

 

 

 

 

Electricity

26449

34.9

35

37

40

43

Biomass

0

0.03

0.032

0.04

0.05

0.08

Utility

14374

4

 

4.4

4.5

4.8

Steam

14374

4

 

4.4

4.5

4.8

Water

0

0.3

 

0.6

0.8

1

Oxygen

0

500

 

600

700

800

Recycled Steel

0

1100

 

 

 

1300

Table A-4 gives the cost benefit technologies observed from this study.

Table A-4 Cost-benefit technologies

Sector

Technologies

Steel

Large-size equipment (Coke oven, blast furnace, basic oxygen furnace etc.); equipment for coke dry quenching; continuous casting machine; TRT; continuous rolling machine; equipment for coke oven gas, OH gas and BOF gas recovery; DC electric arc furnace

Chemicals

Large-size equipment for chemical production; waste heat recovers system; ion membrane technology; existing technology improvement

Papermaking

Co-generation system; facilities for residual heat utilization; black liquor recovery system; continuous distillation system

Textiles

Co-generation System; shuttleless loom; high-speed printing and dyeing

Nonferrous metals

Reverberator furnace; waste heat recovery system; QSL for lead and zinc production

Building materials

Dry process rotary kiln with pre-calciner; electric power generator using residual heat; Colburn process; Hoffman kiln; tunnel kiln

Machinery

High-speed cutting; electric-hydraulic hammer; heat preservation furnace

Residential

Cooking by gas; centralized space heating system; energy-saving electric appliances; high-efficiency lighting

Services

Centralized space heating system; centralized cooling and heating system; co-generation system; energy-saving electric appliances; high-efficiency lighting

Transportation

Diesel trucks; low energy consumption cars; electric cars; natural gas cars; electric railway locomotives

Common Use Technology

High-efficiency boiler; FCB technology; high-efficiency electric motor; adjustable-speed motors; centrifugal electric Fans; energy-saving lighting

 

 

© 2014 IPAC team