Energy Consumption, CO 2 Emissions of Urban Residential Buildings in China and Their Modelling

Energy Consumption, CO 2 Emissions of Urban Residential Buildings in China and Their Modelling Qingyuan Zhang Professor Yokohama National University Japan cho-s@ynu.ac.jp President-appointed Extraordinary Professor, Hiroshi Yoshiro, Tohoku University, Japan, yoshino@sabine.pln.archi.tohoku.ac.jp Abstract Purpose / Context - The purposes of this study are to clarify the trend of unit energy consumption (UEC) and its related CO 2 emissions in urban residential buildings, and to develop models to predict the UEC in the future. Methodology / Approach - Statistical method is adopted to clarify the UEC and related CO 2 emissions. Results (1) The UEC averaged over all regions grows from 14.3GJ/household/year to 23.GJ/household/year during the period of 22-212. Coal and LPG decrease and electricity, district heating increase in percentage. (2) The CO 2 emissions are growing from 2,17 kgco 2/household/year to 3,671 kgco 2/household/year during the period of 22-21. CO 2 emissions by electricity and district heating account for 54% and 35%, respectively. (3) Models are developed to estimate UEC for heating and non-heating regions, respectively. Key Findings / Implications In order to reduce CO 2 emissions by electricity and district heating it is efficient to improve the energy efficiencies of buildings, energy conversion systems, such as boilers, pipelines, generators, etc. It is also important to convert energy source from coal to cleaner energy, such as natural gas. Originality This study makes clear quantatively the energy consumption in the urban residential buildings, and CO 2 emissions caused by the energy consumption. Keywords - UEC, CO 2 emissions, Statistics, Modelling, China. DOI: http://dx.doi.org/1.4225/5/58174282feb HealthyHousing216: Proceedings of the 7 th International Conference on Energy and Environment of Residential Buildings, November 216, edited by Miller, W., Susilawati, C. and Manley, K. Brisbane: Queensland University of Technology, Australia. DOI: http://dx.doi.org/1.4225/5/5817c8eb9c71

1. Introduction In recent years, with the rapid economic growth and improvement of living standard in China, energy consumption has been increasing significantly. Energy conservation is the most important issue for a sustainable society in China. In the residential sector, structure of energy consumption and CO 2 emissions should be clarified for policy makers as well as researchers and engineers. Energy conservation and CO 2 emission reduction may contribute siganificantly to the sustainablility of buildings which is an important topic of the present conference. Some studies have been carried out on residential energy consumption for apartment houses in cities of China. Yoshino et al. have made clear the energy consumption of apartment houses for six Chinese cities by surveying (Yoshino et al., 24); Zhang et al. clarified the energy consumption for the major cities of China in 1997 using official statistics, developing a model predicting energy consumption in the residential houses in China (Zhang et al., 23); Ning et al. investigated the structure of energy consumption by its types in urban and rural areas in China using the Chinese statistics (Ning et al., 27); Ling et al. investigated the consumption of electricity and gas for 23 areas in Beijing and made clear the average energy consumption except energy for district heating (Ling et al., 212). All these studies can be classified into two methods: statistical and survey methods. With the statistical method, researches often face the problem of lacking the items needed for their analyses; but with the survey method, it is difficult to tell if the results can represent the average. Because the statistical method is based on large number of samples, it is adopted in this study. All the studies mentioned above have not been able to make clear the trend of energy consumption in the urban houses, nor the CO 2 emissions caused by the energy consumption. The purposes of this study are to clarify the trend of unit energy consumption (UEC) and its related CO 2 emissions in urban residential buildings, and to develop models to predict the UEC in the future. First, 82 cities are selected as the objects of this study; using the official statistics of the Chinese government, the average unit energy consumption is calculated throughout the period of 22-212. The changes of the UEC and related CO 2 emissions are analyzed. Models are developed to estimate UEC for heating and non-heating regions, respectively. 2. Cities as the objects of research According to the China City Statistical Yearbook (National Bureau of Statistics of China, 22-213a), there are 29 cities in China in total, from which 82 cities are selected as the objects of this study. A list of the cities is shown in Table 1. According to the Thermal Design Code of Residential Buildings (Ministry of Housing Uran and Rural Development of China,1993), China is classified into 5 kinds of regions: Severely cold region, Cold region, Hot-summer-cold-winter Region, Hotsummer-warm-winter Region, and Mild Region as shown in Figure 1. Furthermore, the former two regions are called heating regions because district heating systems are equipped in these regions; the others are called non-heating regions because no district heating system is equipped in these regions in urban planning. Of all the 82 cities to be studied in this paper, 37 cities are located at heating regions and other 45 cities are located at non-heating regions. 3. Unit energy consumption and energy types The energy consumption per capita of different types is calculated using the residential consumption of electricity, coal, coal and natural gases, LPG for each city divided by population in the same city, then energy consumption for each household is calculated by multiplying energy consumption per capita by population per household.

Table 1 Cities to be studied in this paper Heating /Non-heating Provinces Beijing Tianjin Hebei Shanxi Cities 3.1 Average population per household Beijing Tianjin Shijiazhuang,Tangshan, Xingtai, Baoding, Zhangjiakou, Chengde Taiyuan, Yuncheng In. Mongolia Hailaer Heating Liaoning Shenyang, Benxi, Dandong, Jinzhou, Yingkou, Chaoyang regions Heilongjiang Harbin, Qiqihar, Jixi Shandong Jinan, Qingdao Henan Shaanxi Gansu Qinghai Shanghai Jiangsu Zhengzhou, Anyang, Nanyang, Xinyang, Zhumadian Xi'an, Baoji, Yan'an, Hanzhong, Ankang Lanzhou, Tianshui, Pingliang, Jiuquan Xining Shanghai Nanjing, Xuzhou Zhejiang Hangzhou, Quzhou, Lishui Anhui Hefei, Wuhu, Bengbu, Anqing, Buyang Non-heating Fujian Fuzhou, Xiamen, Nanping regions Jiangxi Nanchang, Ganzhou Hubei Wuhan, Yichang Hunan Shaoyang, Yueyang, Changde, Chenzhou Guangdong Guangzhou, Shaoguan, Shantou, Meizhou, Yangjiang Guangxi Nanning, Liuzhou, Guilin, Wuzhou, Beihai Hainan Haikou, Sanya Chongqing Chongqing Sichuan Chengdu, Luzhou, Mianyang, Nanchong, Ya'an Guizhou Guiyang, Zunyi Yunnan Kunming, Baoshan, Shaotong According to China Statistics Yearbook (National Bureau of Statistics of China, 22-213b), the average population per household decreased from 3.47 to 3.16 during the period of 22 212, which shows the size of families in China keeps small because of the birth control policy starting from 197s. 3.2 Consumption of electricity for each household As shown in Figure 2, during the period of 22-212, electricity consumption for each household increased from 1,145 to 2,365 kwh. The main reason for this is the increase of electrical appliance, such as air conditioners, personal computers, etc. Comparing the electricity consumption in Japan which is 5,177kWh/household/year in 212, the level of electricity consumption is still low, and will increase further in the near future with the improvement of living standards in cities of China. 3.3 Consumption of liquid petroleum gas (LPG) for each household As shown in Figure 3, consumption of LPG decreases from 58 kg/household/year to 39 kg/household/year during the period of 22-212, in spite of the increasing trend of energy consumption. The main reason for the decreasing trend is probably the inconvenience when changing the containers.

22 23 24 25 26 27 28 29 21 211 212 Consumption of electricity (KWh/household/year) Figure 1 Region classification on thermal design in China 4 35 3 25 2 15 1 5 Standard derivation Figure 2 Electricity consumption per household 3.4 Consumption of coal for each household Coal was a main energy source in 199s, but has been gradually replaced by gases since then. Because there are no direct data on coal consumption for urban households, the consumption of coal for each household was estimated in a previous study by the authors (Zhang and Yoshino, 216), the results of which are adopted in this paper. During the period of 22 212, coal consumption per household is between 44 to 5 kg/household/year. 3.5 Consumption of natural and coal gases for each household In China City Statistical Yearbook consumption of coal and natural gases is given. The consumption of coal and natural gases is shown in Figure 4. The consumption of natural gas is in a growing trend, while that of coal gas has no obvious change. 3.6 Energy consumption for district heating As mentioned before, China is classified into heating regions where district heating systems are equipped; and non-heating region where there are not district heating systems. In the non-heating regions energy consumed for individual space heating, if any, is included in other types of energy,

22 23 24 25 26 27 28 29 21 211 212 Energy for district heating (GJ/household/year) 22 23 24 25 26 27 28 29 21 211 212 Consumption of gas (m3/household/year) 22 23 24 25 26 27 28 29 21 211 212 Consumption of LPG (kg/household/year) such as coal, electricity, etc. For the heating regions, energy for district heating is calculated from the total energy for heating in the energy balance tables divided by urban populations for each province. As shown in Figure 5, energy consumption for district heating averaged over all regions has been growing from 3.6 GJ/household/year to 9. GJ/household/year, while the value averaged over the heating regions grows from 8. to 18. during the period of 22-212. 3.7 Unit energy consumption As shown in Figure 6, unit energy consumption is calculated by summing different types of energy mentioned above. The UEC averaged over all regions grows from 14.3GJ/household/year to 23.GJ/household/year during the period of 22-212. Coal and LPG decrease and electricity, district heating increase in percentage. 14 12 1 8 6 4 2 Standard derivation 12 1 8 6 4 2 Figure 3 Consumption of LPG per household Coal gas Natural gas 2. Figure 4 Consumption of coal and natural gases 15. 1. 5. All regions Heating regions. Figure 5 Energy consumption for district heating

22 23 24 25 26 27 28 29 21 211 212 Energy Consumption (GJ/Household/year) 22 23 24 25 26 27 28 29 21 211 212 Energy Consumption (GJ/Household/year) 25 2 15 1 5 Coal LGP Natural gas Coal gas Distric heating Electricity Figure 6 Unit energy consumption averaged over 82 cities 35 3 25 2 15 1 5 Coal LGP Natural gas Coal gas Distric heating Electricity Figure 7 Unit energy consumption averaged over 37 cities in the heating regions 4. CO 2 emissions related to energy consumption in residential buildings 4.1 CO 2 emissions by fossil fuels The gas of CO 2 is emitted with the consumption of fossil fuels. CO 2 Emissions by coal, gas, LPG can be calculated using the following equation: U f = { (Q i H i f i )} 44/12 (1) where U f is the CO 2 emissions with the consumption of fuels in residential buildings, kgco2/household; Q i is the consumption of fuel i(m3/household for gases, kg/ household for coal and LPG); H i is the calorific value of fuel i (GJ/ m3 for gases, GJ/kg for coal and LPG), f i is the carbon emission factor of fuel i (kgc/gj) shown in Table 2 (China National Development and Reform Commission, 211). Table 2 Calorific values and carbon emission factor of different fuels Calorific value Carbon emission factor Coal Natural gas Coal gas LPG.29.389.17.518 (GJ/kg) (GJ/ m3 ) (GJ/ m3 ) (GJ/kg) 26.97 15.32 13.58 17.2 (kgc/gj) (kgc/gj) (kgc/gj) (kgc/gj)

22 23 24 25 26 27 28 29 21 211 212 CO2 Emissions (kgco2/household/year) Table 3 Carbon dioxide emission factor by regions Grid Provinces Carbon dioxide emission factor (kgco 2 /kwh) Huabei Beijing, Tianjin, Hebei, Shanxi, Shandong, 1.246 West Inner Mongolia Dongbei Liaoning, Jilin, Heilongjiang, East Inner 1.96 Mongolia Huadong Shanghai, Jiangsu, Zhejiang, Anhui, Fujian.928 Huazhong Henan, Hubei, Hunan, Jiangxi, Sichuan,.81 chongqing Xibei Shaanxi, Gansu, Qinghai, Ningxia, Xinjiang.977 Nanfang Guangdong, Guangxi, Yunnan, Guizhou.714 Hainan Hainan.917 4.2 CO 2 emissions by electricity consumption In China, carbon dioxide emission factor differs from region to region, the value of which is shown in Table 3 (China National Development and Reform Commission, 211). The value for Nanfang Grid is the smallest, and that for the Huabei Grid is the largest. 4.3 CO 2 emissions by district heating According to the energy balance table of China, the energy source of district heating is mainly coal. The efficiency of district heating system including boilers, pipelines, etc. is 7%±1% during the period of 22-212. In this study, the efficiency is supposed to be fixed at 7% when estimating CO 2 emissions. CO 2 emissions estimated by summing the emissions by various types of energy are shown in Figure 8. CO 2 emissions are growing from 2,17 kgco 2/household/year to 3,671 kgco2/household/year during the period of 22-21. CO 2 emissions by electricity and district heating account for 54% and 35%, respectively. It is clear that in order to reduce CO 2 emissions by electricity and district heating it is efficient to improve the energy efficiencies of buildings, energy conversion systems, such as boilers, pipelines, generators, etc. It is also important to convert energy source from coal to cleaner energy, such as natural gas, and so on. 4 35 3 25 2 15 1 5 Distric heating LGP Natural gas Coal gas Coal Electricity 5. Modeling of the UEC Figure 8 CO 2 emissions by different types of energy Zhang et al. developed a model to predict the UEC using heating degree-days, cooling degreehours, energy price, floor area, etc. as parameters (. and Asano, K., 23). Errors in the predicted results occurred because the same model was used for both heating and non-heating regions.

UEC by Eq.(1) In this study, models to predict the UEC are developed for the heating and non-heating regions, respectively. 5.1 Modelling of UEC for heating regions It is known that the unit energy consumption is influenced by weather conditions (heating degreedays, cooling degree-hours, solar radiation in summer and winter), latitude, income, etc. For heating regions, the following model can be created using the least square method: E =.77.1984 φ +.112 HDD +.24372 CDH +.691 I +.593 R 1.71R 7 (2) where E is the UEC in GJ/household/year; φ is the latitude; HDD is the heating degree-days, CDH is the cooling degree-hours; I is the income in YUAN/employee; R 1 and R 7 are the monthly solar radiation in January and July in GJ/month, respectively. The correlation between UECs by Eq.(2) and statistics is shown in Figure 9. The root mean square error from Eq.(2) is 3.43, which shows the UEC in heating regions can be estimated with limited errors. The corelations coefficients of each parameter with the UEC in Eq.(2) are shown in Table 4. Both the latitude and heating degree-days have strong relations with the UEC. Table 4 Corelation coefficients of parameters in Eq.(2) Corelation coefficients of single corelation φ HDD CDH I R 1 R 7.82.7.44.41.71.44 5.2 Modelling of UEC for non-heating regions Similarly, a model for the non-heating regions is developed: E = 14.98 +.324 φ.411 HDD.364 CDH +.566 I.1324 R 1.158 R 7 (3) The correlation between UECs by Eq.(3) and statistics is shown in Figure 1. The root mean square error from Eq.(3) is 3.54, but the decisive coefficient R 2 is as low as.55. One of the reasons for the low decisive coefficient and large error is that no strong factor such as heating degreedays exists and space heating is a personal behavior in the non-heating regions. The residents can either use space heating individually or put on more clothes, which make the estimation of UEC more difficult. 5 4 3 2 1 R² =.89 RMSE=3.23 1 2 3 4 5 UEC by statistics Figure 9 Correlation between UECs by Eq.(2) and statistics in the heating regions

UEC by Eq.(3) 3 25 2 15 1 5 R² =.55 RMSE=3.54 1 2 3 UEC by statistics Figure 1 Correlation between UECs by Eq.(3) and statistics in the non-heating regions Table 5 Corelation coefficients of parameters in Eq.(3) φ HDD CDH I R 1 R 7 Corelation coefficients of single corelation.88.22.9.63.42.15 The corelation coefficients of each parameter in Eq.(3) with the UEC are shown in Table 5. Only income has strong relations with the UEC, while correlation between other parameters and UEC is very weak. The corelation coefficient between heating degree-days and UEC is only.22, which is much smaller than that in the heating regions shown in Table 4. This also demonstrates that residents in the non-heating regions not only rely on heating equipment but also adjust clothing to achieve thermal comfort. The income of residents may influence the decision making in winter on whether or not heating equipment should be used. Because different power grids have different CO 2 emission coefficients which are decided by the types of fuels, it is difficult to develop models to estimate CO 2 emissions in China. 6. Summary In this paper, using statistical data, the unit energy consumption and CO 2 emissions caused by energy consumption in residential buildings are made clear. The main conclusions are as follows: (1) The UEC averaged over all regions grows from 14.3GJ to 23.GJ during the period of 22 to 212. Coal and LPG decrease and electricity, district heating increase in percentage. (2) CO 2 emissions are growing from 2,17 kgco 2/household/year to 3,671 kgco2/household/year during the period of 22-21. CO 2 emissions by electricity and district heating account for 54% and 35%, respectively. (3) Models are developed to estimate UEC for heating and non-heating regions, respectively. 7. Acknowledgment The authors would like to express our gratitude to Prof. Ning Yadong of Dalian University of Technology, Prof. Yang Hongxing of Hong Kong Polytechnic University, Prof. Li Nianpingof Hunan University, Prof. Liu Jing of Harbin Institute of Technology, Dr. Xie Jingchao of Beijing University of Technology for their valuable suggestions and warm encouragement. 8. References China National Development and Reform Commission (211) A guide for the compilation of provincial greenhouse gas inventory Ling, H., Xie, J., Yang, W., Wang, J., Zhu E., Jiang Q.(212) Analyses of Residential Energy Consumption in Beijing, Building Sciences,Vol.28, 266-27

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