1. Introduction
In response to extreme climate change, in 2018 the Intergovernmental Panel on Climate Change released a report showing that the world must limit global warming to 1.5 °C [
1]. Countries can only achieve this goal if they achieve zero carbon emissions by the mid-century. To this end, China announced at the 75th session of the United Nations General Assembly that it would take stronger policy measures to peak CO
2 emissions by 2030 and achieve carbon neutrality by 2060 [
2].
The 2030 and 2060 carbon emission targets provide a concrete timeline for the transformation of China’s energy mix. In view of the current situation in China, China’s energy reform and building energy efficiency work must start from the national situation and find a way to meet its own characteristics. The northern region of China is located to the north of the Qinling–Huaihe line, covering two climatic regions of severe cold and cold regions. Due to the low temperature in winter, the heating in northern towns is mainly concentrated heating. Fifteen provinces and municipalities with a large coverage rate of central heating in northern China are Beijing, Tianjin, Hebei, Shanxi, Inner Mongolia, Heilongjiang, Jilin, Liaoning, Shandong, Henan, Shaanxi, Gansu, Qinghai, Ningxia and Xinjiang. In the northern part of China, due to the winter heating demand, the adjustment of its heating energy use structure and carbon emission reduction is the key to achieving the national carbon emission target. The statistics and prediction of heating energy consumption and the carbon emission data of northern buildings are important for a comprehensive understanding of the current situation of building energy consumption in northern China, discovering the problematic aspects of building energy use, controlling building carbon emissions and guiding the development of building energy conservation work, and the research results can provide strong data support for this.
The current domestic and international energy consumption calculation methods can be divided into two categories: one is the building energy consumption obtained by statistical analysis using survey data, and the other is the building energy consumption obtained by using calculation models based on macroscopic data [
3]. Microscopic data statistics usually adopt the method of sampling survey to conduct statistics on various energy consumption data of various buildings, and the United States, Japan, Denmark and other developed countries have set up special building energy consumption statistics institutions to investigate building energy consumption. The U.S. Energy Information Administration (EIA) divides buildings into two categories, residential buildings and commercial buildings, and conducts surveys and statistics every four years, and then processes the data through regression analysis, engineering models and adjusted estimates to decompose energy consumption into different energy-using terminals [
4]. The U.S. housing metering energy model uses the National Energy Modeling System (NEMS) to analyze and calculate building energy [
5]. The model includes both a top-down macroeconomic model and a bottom-up energy supply and demand model.
The econometric model LEAP [
6], jointly developed by the Stockholm Environment Association and Boston University, USA, can be used as a scenario-based energy-environment modeling tool for energy demand analysis and its corresponding environmental impact analysis and cost-benefit analysis. Based on the LEAP model, Liu, J.L. et al. [
7] developed a building sector energy system model (PECE-Building) by setting three scenarios—baseline scenario (BAU), autonomous contribution scenario (NDC) and enhanced low carbon (ELC)—and analyzed the energy demand and CO
2 emission trends of the building sector under different development paths from 2013 to 2050, concluding that heating is the most important area of emission reduction in northern cities and towns. Li Xinyi [
8] from Chongqing University used cluster analysis and statistical data-based definitions to select residential building prototypes to construct community- and city-scale residential building area energy consumption models around community-scale and urban-scale residential buildings, while using machine learning methods to construct a residential building energy demand prediction tool that took into account the behavioral characteristics of people and future meteorological corrections. Xueling Liu et al. [
9] considered climate and population changes, proposed an evaluation method for per capita energy consumption per unit area and simulated and calculated the cooling and heating energy consumption of various types of buildings in the Tianjin area for the next 30 years according to the different building functions. Sascha Leiber et al. [
10] proposed a novel visual appearance only based heating energy prediction method for single-family houses using powerful image analysis and computer vision techniques that can be used in widely distributed rural buildings in the north of China. The Tsinghua Building Energy Efficiency Research Center has established the China Building Energy Consumption Model (CBEM) that is based on energy intensity and macro-validated by statistical data [
11], yielding a heating energy consumption of 201 Mtce for northern cities and towns in China in 2017, accounting for 21% of building energy consumption.
However, some of these models are from a macro perspective, mainly aimed at fitting the historical data of national energy consumption and carbon emissions, to facilitate the study of the relationship between the energy sector and the overall economy. In addition, the economic development in northern China is not balanced, and the rural area is large. Some models do not separate the rural areas but focus on urban areas. It is impossible to calculate the heating energy consumption and carbon emissions in rural areas more accurately. At the same time, for different building types, accurate division is also very important.
2. Method
Based on the research on macro building energy consumption at home and abroad, this paper calculates the living and public building area by analyzing factors such as population, per capita living area and economic indicators. Starting from the terminal energy consumption, based on the characteristics of China’s energy structure system and building energy consumption, combined with the design and actual operation characteristics of urban central heating and rural heating, this paper establishes a macro prediction calculation model of heating energy consumption of northern civil buildings based on the energy consumption intensity of per capita building area and unit building area. Combined with carbon emissions and future heating energy structure prediction in northern China, the model calculates the total amount of heating carbon emissions in the future, which provides data support for controlling building carbon emissions and guiding building energy efficiency. In the calculation process, considering the heating difference between urban and rural areas, the northern civil buildings are divided into urban buildings and rural residential buildings from the area. Urban buildings include urban residential buildings and public buildings. The total heating energy consumption of urban buildings, including residential buildings and public buildings, and rural residential buildings are calculated respectively, which realizes the attention to urban areas, rural areas and different types of buildings.
The calculation model constructed in this paper is based on the calculation method of building area and unit energy intensity, and after deriving the heating energy consumption, the corresponding carbon emission intensity is then calculated according to the energy structure and carbon emission coefficient. Firstly, the northern civil buildings are divided into urban buildings and rural residential buildings, among which urban buildings are further divided into residential buildings and public buildings. In calculating the building area, a more accurate calculation forecast is made for each type of building area by considering the influence of factors such as housing demand, economic and social development, land resource constraints and urbanization rate. Similarly, when calculating energy consumption intensity, it is necessary to combine factors such as heating methods, heat source efficiency, losses, etc., while taking into account changes in energy policies and changes in heating energy structures, in order to arrive at relatively accurate results. After calculating the floor area and energy consumption intensity, the total heating energy consumption in northern regions can be easily obtained; finally, the heating carbon emissions are calculated based on the energy structure and carbon emission coefficient. The calculation method is shown in
Figure 1.
The research flow of the article is shown in
Figure 2, and the conclusion is in
Section 7.
6. Analysis of the Implementation Path of Heating Energy Saving in Northern Buildings
Based on the existing northern clean heating policies, the energy intensity of urban heating in northern regions under the three scenarios set in this paper has a decreasing trend. In order to facilitate the analysis of energy savings from heating energy efficiency technologies, this part sets a non-energy-efficient heating energy scenario for heating energy consumption in northern towns, i.e., at the current heating energy intensity, the heating energy consumption in future heating of northern towns without any additional measures is compared with the heating energy consumption in a medium control scenario as shown in
Figure 11. The implementation of 65% energy-saving standards for new civil buildings began in 2010, before which the stock of existing buildings in northern towns was about 10.5 billion m
2. Although the total energy consumption of existing buildings and the proportion they account for is gradually decreasing due to their demolition or collapse year by year, due to their huge stock, without energy-saving renovation and operation and maintenance, the heating energy consumption in northern cities and towns will still reach 235 Mtce in 2050. Thus, the energy-saving potential of building heating energy-saving renovation and operation and maintenance is huge.
- (1)
Energy-saving renovation of envelope structure. The building envelope is the part that has the greatest impact on heating energy consumption. At present, the comprehensive heat transfer coefficient of the non-energy-saving building envelope in China is around 1.1~1.3 W/(m
2-K), while the comprehensive heat transfer coefficient of the building envelope that reaches the 65% energy-saving standard is about 0.8~1.0 W/(m
2-K). Currently, some northern regions have started to implement 75% energy efficiency standards, and the integrated heat transfer coefficient of new buildings has been reduced to 0.4~0.6 W/(m
2-K) [
44,
45]. As of 2018, about 60% of urban buildings in northern China had completed an energy-saving renovation, and assuming that all northern urban areas under the medium control scenario complete an energy-saving renovation of existing building envelopes in 2030 and the integrated heat transfer coefficient reaches 0.55–0.75 W/(m
2-K), heating energy consumption will drop to 193 Mtce in 2030. With the update of energy-saving standards, the heat transfer coefficient of the building envelope will be further reduced in the future, and, if the energy efficiency level of 75% is fully achieved in 2050, the heating energy consumption may reach 144 Mtce, which is 92 Mtce lower than the non-energy-saving scenario.
- (2)
Heat source restructuring. From the perspective of heating the heat source structure, heating in northern cities and towns in China is still mainly coal-fired, mainly through coal-fired cogeneration and coal-fired boilers for centralized heating, accounting for about 45% and 30% of heating methods, respectively. According to the requirements of clean heating planning, the scope of centralized heating should be expanded in the future, the proportion of gas heating should be increased, the use of loose coal should be reduced and heat sources such as electric heat pumps, industrial waste heat and renewable energy should be developed for heating. The future adjustment of the heat source structure in northern areas is shown in
Figure 12; energy consumption for heating will reach 233 Mtce in 2030, decreasing to 224 Mtce in 2050, a reduction of 0.12 Mtce over the non-energy-efficient scenario.
- (3)
Heating source efficiency. At present, the heating capacity of cogeneration units has not been fully developed, and the waste heat of spent steam has not been fully utilized; most of the units have a heat-to-electricity ratio below 1.5. While the actual operation can be up to about three times, the heat-to-electricity ratio still needs to be improved. Compared with cogeneration, the boiler efficiency is obviously low. The thermal efficiency of large coal-fired boilers is around 70%, and the efficiency of gas-fired boilers is above 85%, so energy-saving renovation of boiler rooms can effectively improve the heating efficiency of boilers. With the development of science and technology, the efficiency of various heat pumps for heating is also improving. The ground source heat pump COP is usually between 3 and 5, while the heat pump efficiency can be increased to about 8 through the inverter centrifugal technology. Assuming that the heating coal consumption of various heating methods can reach the level of
Table 10 in the future through new energy-saving technologies for heating, the heating energy consumption can be reduced to 213 Mtce in 2050, and the energy saving is about 0.23 billion tce compared with the non-energy-saving scenario.
- (4)
Energy-saving renovation of transmission and distribution pipeline network. At present, the heat loss of China’s urban centralized heating pipe network is more serious, and GB/T 51161 limits the heat loss rate of the pipe network and the index of transmission and distribution consumption, as shown in
Table 11. If the heat loss rate of the pipe network in 2050 under the medium control scenario can be controlled within the district heating constraint value, the pipe network efficiency will reach 95% and the transmission and distribution consumption will drop from 3 to 2 kWh/(m
2-a), then heating energy consumption can reach 219 Mtce in 2030 and be controlled to 192 Mtce in 2050, which is a 44 Mtce reduction compared to non-energy-saving scenarios.
- (5)
Heat metering. Heat metering buildings set temperature control valves indoors. When the indoor temperature is higher than the temperature set by the temperature control valve, the heating system temperature control valve will reduce the opening or close the valve to provide less heat to the room and vice versa to increase the heat supply. However, since its introduction, the heat metering policy has not been popularized due to uneven heat supply, lax monitoring and unreasonable charges. However, heat metering has an important role for heating energy saving. Actual cases show that the annual heat consumption of a centrally heated building has decreased from 87 to 63 kWh/(m
2-a) since the implementation of heat metering retrofit, which is a 28% reduction [
46]. This article assumes that under the medium scenario full heat metering charges will be reached in 2050, the annual heat consumption of urban heating buildings in northern regions will be reduced from 74 to 53 kWh/(m
2-a), and the heating energy consumption will be about 170 Mtce. Compared with the non-energy-saving scenario, the energy saving will be about 65 Mtce.
The heating energy consumption and energy savings in 2050 after adopting the above energy-saving technologies, compared to the non-energy-saving scenario, are shown in
Table 12. However, different energy-saving technologies are interrelated; for example, the improvement of the maintenance structure will at the same time enhance the effect of heat metering and the change in the heat source structure will also affect the heat source efficiency. Therefore, the energy consumption and energy saving in the table are the results of a single action relative to the non-energy-saving scenarios, and the baseline, medium and strict control scenarios in the previous section are a comprehensive consideration of various technology development policy changes, which are reflected in the energy consumption intensity.
Figure 13 compares the energy savings of various energy-efficient technologies for heating in northern towns and cities and clearly shows the difference in energy consumption between the non-energy-efficient scenario and the medium control scenario. It can be seen that building envelope renovation contributes the most to heating energy saving, and building energy saving should focus on this. Heat source structure adjustment has the least effect on heating energy consumption, but, from the perspective of carbon emissions, improving the heat source structure can effectively reduce carbon emissions and promote carbon peaking. The analysis of energy savings of each type of technology in the figure is the result of its single action. In order to achieve the target of urban heating energy consumption under the medium control scenario, all types of energy-saving technologies should also be used in combination with China’s national conditions.
According to the above analysis of various building energy efficiency technologies, combined with China’s building energy efficiency policy measures, in order to achieve the energy consumption target of the medium control scenario, the development path of civil building heating energy efficiency in northern China is shown in
Figure 14.
For the vast rural areas in northern China, the different climatic conditions, geography and economic development should perhaps prompt us to shift our focus from simply reducing the energy intensity of rural heating as a way to reduce carbon emissions to reducing the energy intensity of heating and developing the use of clean energy for heating to reduce carbon emissions. Biomass and solar energy are widely distributed and accessible in rural areas with very different natural and energy conditions. Improving the use of biomass and thus reducing carbon emissions has already been described. Solar energy for rural heating can be divided into active and passive types, according to whether mechanical power is required to drive the elements [
47]. However, due to the low energy flow density, instability and mismatch between the heating characteristics of solar energy and the building load, solar energy is often used in combination with multiple energy sources for complementary heating to enhance the contribution of solar energy in renewable energy and system energy efficiency [
48].
An example is the common coupled solar–air source heat pump system. Chengyang Jiang [
49] et al. proposed a ribbed collector for DXSASHPWH, where the collector can absorb heat in the air and also in the solar radiation, and the average COP of the system can reach 6, which is much higher than the conventional-type system. Shan Ming [
10] et al. selected a demonstration household located in the rural Pinggu District, Beijing; the heating system was a solar hot water collector system plus 4 kW flow inverter low-temperature air source heat pump hot water machine, the end unit used geothermal radiant heating, with the whole heating season solar hot water circulation pump power consumption of about 151 kWh, a low-temperature air source heat pump hot water system power consumption of 6104 kWh and carbon emissions of 2790 kg, much lower than the carbon emissions of traditional heating methods [
50]. Meysam, Huide Fu [
51,
52] et al. designed a PV/T system based on heat pipes by combining cylindrical heat pipes with photovoltaic modules and combined it with a heat pump to design a solar heat pump system based on cylindrical heat pipes, where the average COP of the heat pump system could reach 4.87 and the photovoltaic efficiency was above 11%. The green electricity generated by the PV system can also be used for heat pump heating, thus reducing the carbon emissions from heating. This is promising in provinces such as Tibet, Inner Mongolia and Qinghai, where solar energy resources are abundant. In addition, a solar–geothermal source heat pump coupling system, solar–water source heat pump coupling system, etc. can achieve the same energy-saving and emission reduction effect, which needs to be determined according to the actual local climate, geography and economic situation. Due to the instability of solar energy and the various conditions in different regions, such systems are more complex, and the specific form is difficult to determine, so no further detailed calculations will be made, but rather they will be grouped with other green energy sources under the category of rural renewable energy.