3.1The energy-saving transformation in urban existing residence
The construction area of old residential area in 31 provinces in China is 3.491 billion m2, which in the severe cold region and cold region is 151,165.9 million m2 before August 2015. After the large-scale application and promotion of project achievements, the energy saving and carbon dioxide emission reduction amount of in old residential areas in cold areas is only 10% of the existing old residential area in the severe cold region and cold region. The goal to reduce the building energy consumption ratio set as 10% compared with the guidance value, 600,000 tons of standard coal can be saved annually, 1.5 million tons of CO2 can be reduced, 45,000 tons of SO2 can be reduced, 400,000 tons of carbon dust can be reduced, and economic benefits and ecological efficiency can be achieved. Meanwhile, it significantly improves the livability level of urban existing residence.
the total annual utilization of renewable energy will be 730 million tce, including the utilization of commercial renewable energy will be 580 million tce by 2020, according to the 13th Five-Year plan for renewable energy development [26],issued by the NDRC in December 2016. By the end of 2017, the installed capacity of electric power in China was 1.78 billion kilowatts, of which the installed capacity of renewable energy power generation reached about 650 million kilowatts, which increased from 33.1% in 2015 to 36.6% in 2017. Wind power and solar photovoltaic power generation account for more than 10% of the power generation in Inner Mongolia, Gansu, Qinghai and other provinces, and become an important new power source.
The statistics of energy-saving renovation area of existing residential buildings in China during 2017-2018 are shown in Figure 3-1 From the Retrofitting of Existing Buildings Yearbook of 2018 [27]. In order to promote the revolution of energy production, consumption, and the construction of ecological civilization, the National Energy Administration organized various regions to prepare the development plan of new energy demonstration cities and new energy application demonstration industrial parks, which can give full play to the role of renewable energy in adjusting the energy structure and protecting the environment. The number of key development and utilization of renewable energy demonstration projects recommended by new energy demonstration cities in each province is shown in Figure 3-2. In different cities, the advantages of energy resources are different. The renewable energy mainly considered for development includes solar photovoltaic and heat utilization, shallow geothermal energy, biomass energy (waste power generation, etc.), wind power generation, air energy, marine energy, hydropower, etc.
3.2Energy load in urban existing residence
According to the statistical method of China Building Energy Conservation Association for the energy consumption of existing urban residential buildings, the energy consumption of urban residential buildings is mainly composed of the electricity consumption of household appliances and the consumption of natural gas, which is related to the level of urban development, the level of energy technology, the mode and concept of energy consumption of residents. The per capita energy consumption reflects the basic energy consumption of urban residents, which is positively related to the energy consumption of urban residential buildings.
As shown in Figure 3-3 and 3-4, the per capita usage of electricity and natural gas is increasing, while the per capita usage of coal is decreasing year by year. The per capita use of natural gas has increased by 142.4% since 2009. The per capita energy consumption of the whole country and cities is increasing, and the per capita energy consumption of the cities is higher than that of the whole country, but with the gap decreases , the per capita energy consumption of the whole country is higher than that of the cities in 2017. It can be seen that energy-saving transformation of urban buildings and utilization of clean energy technology have achieved initial results. Figure 3-5 shows that coal is the main source of fossil energy consumption in different region, while electricity and natural gas consumption is the most in cold region.
3.3. Clean energy power production by climate division
The clean energy data changes in the past years according to CHINA ENERGY STATISTICAL YEARBOOK 2018[28]. Specifically, it includes national power generation, nuclear power, wind power, solar photovoltaic power generation, and natural gas production. Data maps and Line charts are provided to explore the advantages of clean energy resources in various regions, providing support for the next step of alternative application of clean energy in urban existing residence.
As shown in Figure 3-6 and 3-7, the electricity generation in each region shows an upward trend. From high to low, the power generation is in the order of cold region, hot summer and cold winter region, severe cold region, hot summer and warm winter region and temperate region. Power generation in cold area is the highest, and the average electricity generation in cold region is 2.7 times higher than that in temperate region.
As shown in Figure 3-8 and 3-9, the hydropower generation in various regions has tended to grow slowly and steadily in recent three years. In hot summer and warm winter region, hot summer and cold winter region, and temperate region, the hydropower generation is more developed than that in other regions. Yunnan and Sichuan province are the best hydropower development due to geographical advantage, which located in the upper reaches of the Yangtze River Basin with huge terrain drop.
As shown in Figure 3-10 and 3-11, the nuclear power generation is mainly concentrated in coastal provinces, including Liaoning, Jiangsu, Zhejiang, Fujian, Guangdong, Guangxi and Hainan. From the perspective of climate division, nuclear power generation is the most abundant in the hot summer and warm winter region, with an increase of 73.3% from 2015 to 2017.
As shown in Figure 3-12 and 3-13, the wind power generation is mainly concentrated in Inner Mongolia, Xinjiang, Gansu, Ningxia, Hebei, Liaoning, Yunnan and other regions. From the perspective of climate division, the wind power generation is the most abundant in severe cold region, the wind power generation in severe cold region increased by 45.8% from 2015 to 2017.
As shown in Figure 3-14 and 3-15, Inner Mongolia, Xinjiang, Gansu, Ningxia and Qinghai are major provinces of solar photovoltaic power generation. From the perspective of climate division, solar photovoltaic energy resources are abundant in severe cold region and cold region, and the solar photovoltaic power generation capacity in the cold region increased significantly in 2017, with an increase of 261.9% compared with 2015.
In summary, it can be seen that: 1) the nuclear power generation capacity in hot summer and warm winter region is ahead of other regions, and the power generation capacity is increasing notably; 2) the wind power generation and solar photovoltaic power generation capacity are strong in severe cold region and cold region, and the power generation capacity is also increasing year by year; 3) the clean energy power generation in each region accounts for a small proportion of the total power generation; 4) the clean energy power generation in each region is positively related to the total power generation.
As shown in Figure 3-16 and 3-17, the natural gas supply areas exceeds 150×108m3 in 2017 include Sichuan, Guangdong, Jiangsu and Beijing. The energy departments in Sichuan Province are encouraged in urban residential buildings to use multi-complementary mode of consumption, such as natural gas preferential price can be reduced to 2.2~2.3 yuan/m3.Natural gas supply in each region shows an upward trend. Natural gas supply in each region ranks from high to low as cold area, cold area, hot summer and cold winter area, hot summer and warm winter region and temperate region. Therefore, the natural gas supply in cold region is the absolute advantage.
3.4 Regression analysis of urban residential energy consumption
The main influencing factors of urban residential energy consumption are total population, per capita energy consumption, GDP and energy consumption per unit of GDP. In this paper, the proportion of clean energy in the terminal energy consumption is added. According to the STIRPAT model in Section2.2.2, the energy consumption model of urban buildings is established as shown in Figure 3-18. Regression analysis is carried out with data from 2009 to 2016. Specific data are obtained from CHINA ENERGY STATISTICAL YEARBOOK 2018 [28], Statistical yearbook of urban and rural construction in China 2018.Beijing: China [29], 2012-2018 Annual Research Report on China’s Building Energy Consumption. Beijing: China [30], as shown in Table 3-1.
Table 3-1. Data for the model
year
|
Energy consumption of urban residential area (104tce)
|
Total urban population
(104)
|
Gross Domestic Product
(108yuan)
|
Energy consumption per unit of GDP
(104yuan/104tce)
|
Urban per capita energy consumption
(10-4tce)
|
Proportion of clean energy in end energy consumption(%)
|
2009
|
23600
|
64512
|
349081
|
1.16
|
328
|
7.9
|
2010
|
24300
|
66978
|
413030
|
0.88
|
320
|
9
|
2011
|
25300
|
69079
|
489301
|
0.86
|
331
|
9
|
2012
|
26800
|
71182
|
540367
|
0.83
|
344
|
9.9
|
2013
|
28800
|
73111
|
595244
|
0.79
|
357
|
10.7
|
2014
|
30100
|
74916
|
643974
|
0.76
|
364
|
11.7
|
2015
|
32000
|
77116
|
689052
|
0.63
|
377
|
12.3
|
2016
|
33900
|
79298
|
743586
|
0.6
|
395
|
13.4
|
Firstly, multiple linear regression is performed on the data, and the multicollinearity of the data is obtained by Klein discriminant method. Therefore, multiple linear regression cannot be used, ridge regression is used as mentioned above. Ridge regression is a biased estimation regression method which is specially used in the analysis of collinear data. After running, the ridge trace is shown in Fig. 3-19.According to the value principle of K value (lambda value), this paper takes K value (lambda value) recommended by R software. K = 0.01215678,The regression equation is as follows:
where, P is Total urban population, GDP is Gross Domestic Product, EPG is Energy consumption per unit of GDP, EP is Urban per capita energy consumption, CE is Proportion of clean energy in end energy consumption.
The results show that the coefficient of determination R2 (multiple R-squared) is 0.9984 and 0.9950 after correction. The model has high goodness of fit and the model is established in Equation 3. The result is from large to small in order to their influence is per capita energy consumption, proportion of clean energy in energy terminal consumption, total population, energy consumption per unit of gross product and total population. The fitting results show that this factor accounts for a large and significant proportion in the influencing factors of existing urban residential buildings, which means that the utilization technology of clean energy will be a very important direction for the transformation of urban existing residence. Previous data analysis shows that the energy consumption of urban residential buildings increases year by year with the increase of GDP and energy consumption per capita. Compared with 2009, the energy consumption of urban residential buildings increases by 43.6% in 2016.Natural gas has also increased from 7.9% to 13.4%. But this still cannot meet the demand of energy consumption in urban residence.