Lifestyles and Energy Consumption in Households

Transcription

1 Proceedings of International Workshop on Policy Integration Towards Sustainable Urban Energy Use for Cities in Asia, 4-5 February 3 (East West Center, Honolulu, Hawaii) 3 Institute for Global Environmental Strategies All rights reserved. Lifestyles and Energy Consumption in Households Toru Matsumoto *a, Jian Zuo b, Xindong Wei c. Introduction According to an estimate by the United Nations in 99, the urban population ratio in Asia in 99 was 35.%; however, the number is expected to grow to 55.% in 3. Urbanization in Asia has the distinct characteristic of taking place in large-scale urban areas. In terms of mega-cities that have a population of over million, it is expected that of them will come into existence on the planet by 5, and 8 of that estimated number will be in Asia. As a synergistic effect of the further expansion of Asian mega-cities and an increase in peoples income, energy consumption by residential and commercial sectors will drastically increase. Not even considering air population issues in urban areas, urban activities hold an important key for future global warming issues also. In this report, we will take a closer look at energy consumption by residential and commercial sectors in the Asian mega-cities. Prediction models of energy consumption in residential and commercial sectors were developed for,,, and Shanghai until. Table explains the general states of the four cities on population, area and population density. First, we need to clearly define the term urban. In order to accumulate data at the urban level as to the greatest degree possible, the term must include metropolises and districts in Japan, metropolises and cities in Korea, and also greater regions, which include urban and rural areas in China. As the table shows in its comparison of the metropolitan scales of the metropolis, the metropolis, and the greater region, has a distinctly larger area than the other two, but the population is on a similar scale. Table. Comparison of the 4 cities on population, area and population density Shanghai Population ( 3 ),59,373,78 3, Area (sq.km),,88,34 Population density (persons/sq.km) 5,737 7,3 7,84 Data source: Statistical Yearbook, Statistical Yearbook, Statistical Yearbook of, Statistical Yearbook of Shanghai. * Corresponding author. Tel: , Fax: , matsumoto-t@env.kitakyu-u.ac.jp a Associate Professor, Department of Environmental Space Design, Faculty of Environmental Engineering, the University of Kitakyushu, Japan. b Research Fellow, Department of Environmental Space Design, Faculty of Environmental Engineering, the University of Kitakyushu, Japan. c Doctral Student, Department of Arctecture, Graduate School of Engineering, Waseda University, Japan

2 . Lifestyles and energy consumption Figure shows the floor space per, and Figure shows the size of. In all of the four urban areas, the floor space per is becoming larger, and the number of persons per is becoming smaller. Both trends are general factors of energy consumption increase per person. (m /) 4 8 Shanghai(Rural) (Rural) 4 (Urban) Shanghai(U rban) Figure. Trends of flour space per (persons/.) (U rban) B eijing (R ural) Shanghai Figure. Trends of size of Refrigierator(/ s) 8 (rural) 4 Shanghai(urban) Shanghai(rural) Figure 3. Diffusion rate of refrigerator Air condition (/ s) Shanghai(urban) (urban) Shanghai(rural) Figure 4. Diffusion rate of air conditioner Color TV (/ s) 8 4 B eijing (urban) 8 Shanghai(urban) (rural) 4 Shanghai(rural) Microw ave oven (/ s) (urban) Shanghai(urban) Figure 5. Diffusion rate of color TVs Figure. Diffusion rate of microwave oven Figures 3- indicate diffusion rates of electrical appliances in, Shanghai, and. According to the Figure 3, diffusion rate of refrigerator is almost % in urban areas and approximately 8% in rural areas in

3 and Shanghai. Secondly, diffusion rate of air conditioners are % in, approximately 9% in urban areas of Shang hai, and approximately % in urban areas of (Figure 4). Even in terms of color TVs and microwave ovens, ur ban areas in Shanghai and show numbers that are reaching very close to the standard of (Figure 5,). ( TO Es) Shanghai Figure 7. Total residential energy consumption (TO E/) Shanghai Figure 8. Residential energy consumption per Figure 7,8 indicate transitions in total energy consumption, and energy consumption per in residential sector. has the highest number in both categories. has a declining trend of total energy consumption and an almost unchanged trend of energy consumption per. On the other hand, in and Shanghai, energy consumption per increased slightly, but total energy consumption increased drastically. Figure 9 indicates transitions in residential energy consumption ratio by fuel type in the four mega-cities during the period 99-. shows a quite evident decline in coal and an increased diffusion of town gas. Even though coal use has been declining in both and Shanghai, has a drastically increased consumption rate of natural gas. % 8% % 4% % % E le c tricity Heat Diesel H eavy oil Kerosene N aturalgas LPG C oke gas Tow n gas Coal S hanghai Figure 9. Residential energy consumption by fuel type

4 3. Basic concept and model structure 3.. Basic concept Asian cities should be studied with consideration of two points. One is data acquisition. Considering this, the refinement of the structure of a model appears limited. In particular, more data is available for than for any other city, but model construction based on is not permitted. The other is to keep up with the varying rates of growth in the region. Economic growth and its accompanying change in living standards or enhancement of technical standards means that not many parameters can be handled as fixed values. This makes it necessary to ensure that the structural parameters of a model structure are variable. Therefore, the authors will develop a model satisfying these two points and predict the energy consumption and carbon dioxide discharge in the residential/commercial sector in. 3.. Model structure Figures and demonstrate the analytical flows of energy demand in projection models for the resdiential sector of and the residential sectors of,, Shanghai, due mainly to the availability of energy consumption data by use types. Since data regarding energy consumption by use type and fuel type in was available, energy consumption per fixed floor space or by use type has been estimated, and then this estimated value of energy consumption by use type was divided by fuel types. For estimating the amount of energy consumption by use type, multiple regression analysis with explanatory valuables was utilized. For the other three mega-cities, since statistics for the amounts of energy consumption by use type and fuel type were unavailable, only the amounts by fuel types were estimated. has a widely diffused district heating system (DHS), thus heat is estimated separately. Further, the urban areas and rural areas have quite different energy consumption structures, which are estimated separately in. Data collection & composition of variable multiple regression Estimation of energy demand per basic unit by demand types Basic unit * Projection of energy demand by demand types Divide into fuel types Data collection & composition of variable multiple regression, etc. Estimation of energy demand per basic unit by fuel types Basic unit * Projection of energy demand by fuel types Projection of CO emission Projection of CO emission Scenario analysis for CO reduction * Basic unit = flour space or Figure. Analytical flow for residential sector of Scenario analysis for CO reduction * Basic unit = Figure. Analytical flow for residential sectors of, and Shanghai

5 4. Development of energy demand model for residential sector 4.. Analytical procedure As the Figure and Figure show, the energy demand for each use is estimated and decomposed by usage and fuel types. The uses of energy in each division were classified into heating, cooling, 3hot-water supply, and 4lighting, driving, and other uses. The energy demand for each use is expressed as the following identity with intermediate terms: FL ENE. R ENE. R = HS () HS FL where ENE.R is energy consumption by demand type for residential sector, HS is number of, FL is floor space. The second intermediate term represents the floor space per and the third intermediate term represents the energy demand per unit floor space. If floor space data is not available or the future floor space is directly available, the formula below is appropriate. The formula will also be used to predict the energy demand for heating not dependent on floor space. ENE. R ENE. R = HS () HS The second intermediate term represents the energy demand per. The procedure up to the construction of the prediction model is outlined below. STEP When necessary, synthesize the explanatory variables of energy demand per unit floor space, more specifically, energy price, equipment possession rate, and equipment energy efficiency. These variables are unique to the equipment or energy type. For use as explanatory variables of energy demand per unit floor space by uses, synthesize the variables into the average value for each use. STEP To estimate the second and third intermediate terms of Formula (), evaluate the variable factors by multiple regression analysis. From the results, formulate a model for predicting the floor space per and the energy demand per unit floor space. STEP3 Prepare predictive values for the explanatory variables of the model formulated in STEP. Thus, the model is used to calculate predictive values of the energy demand by uses until. STEP4 Decompose the values estimated in STEP3 by fuel type (electricity, kerosene, city gas, and LPG). To do so, an energy demand matrix for each fuel type by use should be prepared. Estimate the matrices of electricity, kerosene,

6 and gas (city gas + LPG) by use from the trends of the past 5 years. With regard to the breakdown of gases, city gas consumption was predicted with the estimated future diffusion of city gas as an explanatory variable. The remainder is LPG. STEP5 Estimate the energy demand by fuel type and multiply the energy demand by the unit requirement of carbon dioxide discharge to predict the carbon dioxide discharge until. 4.. a. Energy demand for heating Figure shows the method for estimating the energy demand for heating. To estimate the requirement of energy consumption per unit area, variable factors were evaluated by multiple regression analysis. Consequently, the heating degree-day, the heating energy price, the house insulation factor, and the amount of heating equipment per unit floor space were adopted as four variables. The regression formula obtained this way was adopted as a prediction model. As an explanatory variable, the amount of heating equipment differs greatly depending on the equipment type (air conditioner, kerosene stove, or fan forced heater) and also between single and multiple occupancy s. Therefore, the amounts of heating equipment by equipment and type were synthesized from the energy efficiency of each model and the number of s by type. To estimate the floor space per, a formula was created from the number of persons in a and the compensation of employees per. This also applies to the energy demands for cooling and hot-water supply. Possession of device Climate Insulated housing Energy price Employees income per Size of Floor space per Energy consumption per floor space Number of Energy consumption for heating Future forecasting Diffusion of town gas Energy demand matrix Electricity Town gas LPG Kerosene Figure. Estimation of residential energy consumption for heating in

7 The above explains the past energy consumption for heating but future parameter settings are necessary for prediction. As to the house insulation factor, the time series trend of the slowdown of growth was predicted by using an exponential curve. The equipment diffusion per unit floor space was predicted from the compensation of employees per. The energy price and the heating degree-day were adopted from past averaged data. With regard to the number of s and the number of persons per, values estimated by the National Institute of Population and Social Security Research were used. Table shows the adjustment methods of the future scenario for the external variables. Table. Adjustment of the future scenario for the external variables - External variables No.of s No of persons per Compensation of employees per Cooling degree day (CDD) Heating degree day (HDD) Future scenario for external variables as estimated by the National Institute of Population an Social Security Research as estimated by the National Institute of Population an Social Security Research Assuming the average figure for the increase rate of the employment income per head during , to be constant in future, calculate the prospective employment income per head. Multiply the figure achieved by estimated value of the number of members, and set this as a prospective employment income per. Averrage valuse of the Energy price Average value of the Water consumption per the water consumption per capita during the period was linearly approximated and prospected to increase with the same rate. Multiplying this by prospected number of people per and put this as a prospective amount of water oer. By multiplying the energy consumption per unit floor space, the floor space per, and the number of s estimated according to Formula (), the predictive value of energy consumption was calculated for heating. The calculation method depends on the fuel type as explained in STEP4 of 4.. b. Energy demand for cooling Figure 3 shows the method for estimating the energy demand for cooling. To estimate the unit requirement of energy consumption per unit floor space, variable factors were evaluated by multiple regression analysis. Consequently, the heating degree-day, cooling coefficient of performance (COP), and the amount of cooling equipment per unit floor space were adopted as variables. As for heating, the cooling energy price, the cooling COP, and the amount of cooling equipment per unit floor space were weighted with energy consumptions by cooling equipment and averaged as synthesized variables. The regression formula obtained in this manner was adopted in the prediction model. Future values of parameters were established for prediction. The cooling COP was evaluated using a time-dependent logistic function. The amount of cooling equipment per unit floor space was predicted by the floor space per. For the cooling degree-day, the average value of past data was adopted as for heating.

8 Possession of device Climate Efficiency of device Employees income per Size of Floor space per Energy consumption per floor space Number of Energy consumption for cooling Future forecasting Electricity Figure 3. Estimation of residential energy consumption for cooling in By multiplying the energy consumption per unit floor space, the floor space per, and the number of s estimated according to Formula (), the predictive value of energy consumption for cooling was calculated. The energy for cooling is electricity only and needs not be decomposed by fuel type. c. Energy demand for hot-water supply Figure 4 shows the method for estimating the energy demand for hot-water. The energy demand for hot-water supply was calculated using Formula () because it does not depend significantly on the floor space. To estimate the unit requirement of energy consumption per, variable factors were evaluated by multiple regression analysis. Consequently, the hot-water supply energy price, the insulation factor, and the water consumption per were adopted as variables. The energy price is a synthesized variable. The house insulation factor was considered to average the performance of hot-water supply equipment as a proxy variable of new diffusion. The regression formula obtained in this manner was adopted as a prediction model. As to the future values of parameters, the water consumption per person was obtained by linear regression on the assumption that the tendency of a slight increase in the past 5 years would continue. From the energy consumption Insulated housing Energy price Water consumption per Energy consumption per Number of Energy consumption for hot-water Future forecasting Diffusion of town gas Energy demand matrix Electricity Town gas LPG Kerosene Figure 4. Estimation of residential energy consumption for hot-water supply in

9 and the number of s estimated above, the predictive value of energy consumption for hot-water supply was calculated and decomposed by fuel type according to STEP4 of 4.. d. Energy demand for lighting, power, and other uses Figure 5 shows the method for calculating the energy demand for lighting, power, and other uses. To estimate the unit requirement of energy consumption per unit floor space, variable factors were evaluated by multiple regression analysis. Consequently, the lighting, power, energy price and the refrigerator equipment efficiency were adopted as variables. The energy price is a synthesized variable. The future values of parameters were then established. With regard to equipment efficiency, the future value was estimated as a time-dependent logistic function. By multiplying the energy consumption per unit floor space, the floor space per, and the number of s estimated according to Formula (), the predictive value of energy consumption for lighting, power, and other uses was calculated and decomposed by fuel type according to STEP4 of 4.. Possession of device Energy price Efficiency of device Employees income per Size of Floor space per Energy consumption per floor space Number of Energy consumption for lighting, etc. Future forecasting Diffusion of town gas Energy demand matrix Electricity Town gas LPG Kerosene Figure 5. Estimation of residential energy consumption for lighting, etc. in 4.3. a. Outline of model The model was constructed by categorizing energy consumption into three parts; ().heating systems and hot water supplies, (). kitchen, and (3). lighting, power, air cooling systems and others. The future (prospected) value of the explanatory variables will be established by using the method in Table 3.

10 Table 3. Adjustment of the future scenario for the external variables- External variables Income per Future scenario for external variables The future income per was calculated by multiplying income per head by the number of members. Income per person was estimated by the equation attained from the correlation analysis respect to GRP per person. Consumption expenditure per Energy consumption price index The future consumption expenditure per was calculated by multiplying consumption expenditure per person by the number of member. Consumption expenditure per person was attained by a correlation analysis respect to GRP per person. The future energy consumption price index was predicted by keeping the average value of the energy expenditure price index. b. Energy demand for heating and hot water Figure shows the estimation flows of energy consumption for heating and hot water supplies. Floor sapce per was estimated on the basis of the number of persons per and income per. The energy consumption rate for heating was 38kwh/m *year, that was the estimated value of energy consumption of heating per floor space in 99. (Ralf Ulseth) Persons per Consumption expenditure per Floor space per Energy consumption per unit of floor space Number of s Energy consumption for heating and hot water Coal Petroleum Gas Figure. Estimation of residential energy consumption for heating and hot-water in According to Ojima, the energy consumption for hot water per floor space in varied, ranging from.9 (condominium) to.3 (private single residence) Mcal/m *year, and its average value,.mcal/m *year, was applied to. c. Energy demand for cooking Figure 7 shows the estimation flows of energy consumption for cooking. Multiple regression analysis was applied, and explanatory variables of energy consumption per were consumption expenditure per, income per and the number of people per.

11 Persons per Income per Consumption expenditure per Energy consumption per Number of s Energy consumption for cooking Coal Petroleum Gas Figure 7. Estimation of residential energy consumption for cooking in d. Energy demand for lighting, power, cooling and other uses Figure 8 shows the estimation flows of energy consumption for lighting, power, cooling and other uses. The energy price of lighting, power, cooling and other uses, consumption expenditure per, and income per were used as explanatory variables for the energy consumption per. Consumption expenditure per Income per Indicator of energy price Energy consumption per Number of s Energy consumption for lighting, power, cooling and others Electricity Figure 8. Estimation of residential energy consumption for lighting, power, cooling and etc. in e. Perspectives of proportion of the energy consumption by fuel type Figure 9 indicates the estimation flows of the residential energy consumption and amount of CO emission. This was done by calculating the residential energy consumption where all the previously estimated values of residential energy consumption by type of uses were added, followed by the sorting of this figure by types of fuels. The proportion of gas consumption was obtained by extrapolating the trend of the 9-year gas consumption data for the period The prospective value of the proportion of coal consumption was obtained from a correlation equation with respect to past gas consumption, and the remainder was taken as oil consumption. Electricity consumption was assumed as the predicted result for lighting, power, cooling and other uses of energy consumption.

12 Moreover, by adopting the past trend, gas was divided into town gas and LPG consumption, oil into keroseno, heavy oil and diesel. Energy consumption for heating and hot water Energy Consumption for cooking Energy consumption for lighting, power, cooking and others Share of gas consumption Share of coal consumption Household energy consumption by use Share of petroleum Gas consumption Coal consumption Petroleum consumption Electricity Consumption Urban gas consumption LPG consumption Kerosene consumption Heavy oil consumption Diesel consumption CO emission rate CO emission Figure9. Estimation of residential energy consumption and CO emission in 4.4. a. Outline of model Due to the unavailability of data on energy consumption by use type in, it was estimated by fuel type (Beijng Municipal Statistical Bureau, Department of industry and transport statistics, national bureau of Statistics, P.R. China). For this purpose, it was necessary to consider the matching of uses of energy and fuel type, and its appropriate explanatory variables (Beijng Municipal Statistical Bureau). In urban areas of, district heating systems (DHS) and individual heating using coal are the most general methods for heating in winter. There are also ones generated by electricity and gas, however, due to the small amounts used, these are ignored here. Gas used for hot water and cooking will be divided into LPG, natural gas and coking gas after estimating the total amount of gas consumpiton. Electricity is estimated on the basis of income per. In the case of rural areas, only commercial energy was taken into account, on the basis of which the consumption of coal and electricity were estimated. b. Heat from DHS and coal for individual heating in urban area Both these relate to the urban areas. The heat from DHS can be obtained by calculating the heat consumption per unit of heating space by applying the following Formula (3) (Song, X. H. and N. Moriyama), and then multiplying it by the heating area of DHS (Figure ). ti t p Q = Z 4 3 q. 39 (3) ti tw

13 No. of urban s Percentage of s using DHS Floor space per Total floor space of s using district heating system Heating space conversion rate Heat consumption rate of DHS Heating space Heat consumption Figure. Estimation of heat consumption in (DHS) Table 4. Adjustment of the future scenario for the external variables - External variables Floor space per Future scenario for external variables Floor space per was estimated by a a linear equation based on a correlation analysis to income per househod (the 98- data). Future income per was calculated by multiplying income per person by the number of members. Income per head was estimated by the correlation equation (logistic curve) achieved by the correlation analysis respect to GRP per person. No. of s in urban area The proportion of the number of s in urban area was predicted by applying the 988- time-series trends respect to its linear equation. The perspective number of s of the urban area was estimated by multiplying the previously achieved figure by the estimated total number of s. Urban Household diffusion rate of This was estimated based on the district heating system plan in until. district heating system Floor space of DHS user This was calculated by multiplying floor space per by the number of s in urban area and diffusion rate of regional heating system Rural Conversion coefficient into heating space The conversion coefficient for heating space was calculated by dividing DHS heating space by floor space of the of DHS. The average value of the 995- data remained to be constant. Household diffusion rate of Calculated by subtracting diffusion rate of regional heating from %. individual heating Gas diffusion rate Future value was set to be % The number of s in rural area Income per Subtracting numbers in urban area from number. Future predicted income per was calculated by multiplying income per head by the number of members. where, Q : annual heat consumption per square meter heating area (TOE/m ) Z: heating days

14 q: heating design load (W/m ) t i : indoor heating calculating temperature t p : average outside temperature during which the heating system is in operation t w : outdoor average temperature during heating period As can be seen from the Figure, heating sapce supplied by DHS was calculated from the number of s, the diffusion rate of DHS s, floor space per and conversion coefficient from floor sapce to heating space. The predicted value for explanatory variables was calculated by applying the method noted in the Table 4. As for coal used for individual heating in urban area, it can be obtained by calculating the coal consumption per unit of heating space by applying the following Formula (4), and then multiplying it by the heating area of individual heating (Figure ). Q = η D (4) η where D: annual coal consumption per square meter of heating area (TOE/m ) η : boiler efficiency η : transport efficiency of piping Heat consumption rate of individual heating Urban Heating space conversion rate Transport efficiency of pipe Coal consumption per unit of heating area Heating area Percentage of s using individual Boiler efficiency Coal consumption for individual heating Coal consumption for other use No. of urban s Living space per Coal consumption Coal consumption of rural s Coal consumption per Rural No. of rural s Figure. Estimation of residential coal consumption in

15 c. Coal used in urban area (excluded the part for heating) and coal used in rural areas Coal consumption occurs not only in the case of individual heating in urban areas, but also for cooking and hot water supplies by the fluid population, concentrated hot water supplies, and communal baths. The estimated figure for 995-, obtained by subtracting coal for individual heating and cooking from coal consumption in urban areas, reveals that it decreased from 79, in 995 TOE to 35, TOE in. In order to predict the future figure, it was assumed that it decreases at a constant rate from the amont in the year. In addition, it was assumed that the decreased amounts were to be replaced by gas. Regarding coal consumption per in rural areas, an average value for the period 995- figures was taken as the coal consumption rate for future prediction (Figure ). By multiplying the rate by the number of s in the rural area, coal energy consumption was estimated. d. Gas Today, gas is used only in the urban area of. To obtain the gas consumption per, multiple regression analysis was used and variable factors were analyzed. As the result, the possession rate of electrical cookers was chosen (Figure ). No. of electric cooking appliances owned Income per Gas consumption per Household using gas Gas consumption Urban Percentage of s using gas Matrix of gas supply by type Natural gas Coking gas LPG Figure. Estimation of residential gas consumption in Urban Rural Income per urban Income per rural No. of color TV owned Electricity consumption per urban No. of urban s Electricity consumption per rural No. of rural s Electricity consumption Electricity consumption Electricity consumption Figure 3. Estimation of residential electricity consumption in

16 e. Electricity Similarly, in order to estimate the electricity consumption per, multiple regression analysis was used and the variable factors were examined. The results made it necessary to use income per for urban areas, and in the case of rural areas the number of colour televisions owned and income per were adopted (Figure 3). 4.5 Shanghai a. Outline of model In, about 99 percentage of the total population in Shanghai are urban residents, therefore the investigation was carried out by combining both urban and rural areas. The uses of energy consumption were grouped roughly into () the use for cooking and hot water, () the use for lighting, power, air conditioners. Table 5 shows the approaches on prediction of the explanatory variables. Table 5. Adjustment of the future scenario for the external variables - Shanghai External variables Population Persons per GRP Income per Future scenario for external variable Predicted by the logistic curve of time-series trend based on the 98- data. Predicted respect to the comparison with the population time-series trends in Calculated by assuming the increase rates of GRP, both the same in Shanghai and Future income per was calculated by multiplying income per person by the no. of persons per. Income per head was estimated by the correlation equation (logistic curve), achieved the correlation analysis respect to GRP per person b. Energy demand for cooking and hot water Figure 4 illustrates the estimation flow of energy consumption for the uses of cooking and hot water supplies. The energy consumption for cooking and hot water per was calcualted based on the 98- data on coking gas, LPG, coal consumption and the number of s, and no notable change was seen in figures for that period of years. Therefore, the average value of the most recent 5 years was used as ernegy consumpiton per in the future. Per energy consumption for cooking and hot water No. of s Energy consumption for cooking and hot water Energy supply matrix by type Natural gas Urban gas LPG Coal Figure4. Estimation of residential energy consumption for cooking and hot- water in Shanghai Between 98 and, the proportion of coal consumed decreased sharply from 87 per cent to 43 per cent. However the proportion of coking gas used increased from per cent to 37 per cent, and the LPG use increased from

17 less than per cent in 98 to 3 per cent in 995, and then have been maintained at the level of 5 per cent. In the future, although the exploitation of natural gas is expected to increase further due to the attainment of the West-East Natural Gas Transmission Project. Although coal consumpiton will keep decreasing with the change of ernegy cosnumption structure, it is still used as residential energy for fluid population, part of population in rural areas and, fuel for concentrated hot water supply and communal baths. c. Energy demand for lighting, power and air conditioning etc Figure 5 illustrates the estimation flow of energy consumption used for lighting, power, air conditioners, etc. By multiple regression analysis, the number of air conditioners, colour televisions and microwaves possessed were adopted as explanatory variables for electricity consumption per. Income per No. of home electric appliances owned (air condition, color TV and microwave oven ) Electricity consumption per No. of Electricity consumption Figure5. Estimation of residential electricity consumption in Shanghai 4.. Projection of energy consumption and CO emissions Figure shows the estimated results of future total residential energy consumption and energy consumption per in the four mega-cities by. has a large percentage of increase, and will become the second largest energy consuming mega-cities after in. In contrast, for both the total amount of energy consumption and energy consumption per, the trend in has changed from a sideways movement to a declining tendency since. ( TO Es) 7 5 Shanghai (a) Total energy consumption (TO E/) Shanghai Figure. Projection of residential energy consumption (b) Per energy consumption The incidence of air pollution caused by the exploitation of the natural gas in Talimulantai county in Xinjiang Uighur Autonomous Region has spread, streamed by air to the Shanghai area.

18 ( TO Es) Kerosene 5 LP G 4 Town gas 3 Electricity ( TO Es) Electricity Diesel H eavy oil Kerosene LPG Tow n gas Coal ( TO Es) Electricity Heat Nauturalgas LPG Tow n gas Coal ( TO Es) S hanghai Electricity NatrualGas Tow n gas LPG Kerosene Coal Figure 7. Projection of total residential energy consumption by fuel type ( t-c O ). 5 5 Shanghai Figure 8. Projection of total CO emission of energy consumption in residential sector Figure 7 shows the divided amounts of fuel types of future total residential energy consumption in the four mega-cities by. In,, and Shanghai, the amount of coal use is declining, and the amount of town gas and natural gas (LNG) use is increasing. Even though the percentage of electric energy consumption has not changed

19 much, this is due to the fact that the results do not sufficiently express transitions to electric energy consumption for heating, hot water supply, and cooking. Figure 8 shows future expected results of CO emissions of energy consumption in residential sector. One remarkable difference from the expected amount of energy consumption is that and Shanghai are positioned above and. 5. Development of energy demand model for commercial sector 5.. Analytical procedure Figure 9 is an analysis flow for projected models of future commercial energy demand. The future estimated values are calculated based on the tendencies in energy consumption units per GRP for tertiary industries, and then the estimated future values of GRP for tertiary industries are multiplied to form the results. The identity can be expressed as Formula (5) shown below..the amount of energy consumption for each of the fuel types is calculated by utilizing the trends in energy consumption, which are also based on fuel types. ENE. C ENE. C = GRP3 (5) GRP3 where ENE.C is commercial energy consumption. GRP3 is the GRP of tertiary industries. The second intermediate term denotes the energy demand per unit GRP of tertiary industries. Data Collection Prediction Prediction Energy Consumption per Unit of Tertiary Industry GDP Total GDP Share of Tertiary Industry GDP to Total GDP Total Commercial Energy Consumption Matrix of Energy Consumption Commercial Energy Consumption by Fuel Types Prediction of CO Emission Figure 9. Analytical flow for commercial sector

20 5.. Projection of energy consumption and CO emission Figure 3 shows the future expected results of the amount of commercial energy consumption and the amount of energy consumption per GRP of tertiary industries in the four mega-cities by. The amount of energy consumption per GRP of tertiary industries in and Shanghai tends to decrease; however, the results indicate that the amount of commercial energy consumption in those areas will exceed the amount in by around. Figure 3 shows the divided amounts of fuel types for future commercial energy consumption in the four mega-cities by. All of the mega-cities have a large percentage of electric energy consumption, and the ( TO Es) Shanghai (T O E / m il.995usd ) Shanghai (a). Total energy consumption (b). Energy consumption per GDP of tertiary industries Figure 3. Projection of commercial energy consumption ( TO Es) Electricity Town Gas LPG H eavy O il Kerosene ( TO Es) Others Heat Electricity Town Gas LPG Other Oil H eavy O il Kerosene Diesel Gasoline Coal ( TO Es) Others Electricity Heat C oke gas N aturalgas LPG Disel Kerosene Gasoline Fueloil Coal ( TO Es) 4 S hanghai Others Electricity Coke Other Oils Disel Kerosene Gasoline Fueloil Coal Figure 3. Projection of commercial energy consumption by fuel type

21 increasing tendency will continue in the future. Figure 3 shows future expected results of CO emissions from commercial sectors. One remarkable difference from the expected amount of energy consumption is that and Shanghai are positioned above and as well as residential sector. ( t-c O ) Shanghai Figure 3. Projection of total CO emission of energy consumption in commercial sector. Future directions For application to Asian mega-cities, the authors developed a model for predicting the energy demand in the residential/commercial sectors of. The analysis so far enabled for the prediction of a so-called trend case. The next step is to estimate the effects of various measures and the influence of social trend changes using the model. The subjects in the home division can be classified mainly into and lifestyle factors, architectural characteristics, and 3characteristics of energy devices. For trial calculation, the influence of these scenarios on the unit requirements of energy consumption (per unit floor space and per ) calculated in Chapter 4 was analyzed. As to, the influence of property changes can be experimentally calculated if the energy consumption characteristics by can be gained through questionnaire surveys. The change in the time spent at home due to changes of work patterns may be another factor that can be experimentally calculated as a change from the assumed trend case in the same way. References Municipal Statistical Bureau statistical yearbook (99-), China Statistics Press. Beijng Municipal Statistical Bureau Two decades of reform and opening to the outside in China Statistics Press. Beijng Municipal Statistical Bureau years of. China Statistics Press. Department of industry and transport statistics, national bureau of Statistics, P.R. China. 99. China energy statistical yearbook 989. China Statistics Press. Department of industry and transport statistics, national bureau of Statistics, P.R. China. 99. China energy statistical yearbook 99. China Statistics Press.

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23 Tanaka, A., K. Sakai, H. Nakagami, M. Chiharu, O. Ishihara.. A Study on the estimation of energy consumption by type, Journal of the Architectural Institute of Japan, no.539: The energy data and modeling centre, the Institute of Energy Economics, Japan, Handbook of energy & economic statistics in Japan. The Energy Conservation Centre, Japan Metropolitan Government, Bureau of General Affairs, Statistics Dept. living survey United Nations World urbanization prospects: The 99 Revision.