Carbon Mitigation in the Operation of Chinese Residential Buildings: An Empirical Analysis at the Provincial Scale

Abstract

The rapidly growing carbon emissions of residential building operations have become an obstacle to China’s commitment to achieving its carbon-neutral goals by 2060, but they also demonstrate great carbon mitigation potential. To help buildings reach carbon neutrality targets, this study decomposes the drivers of carbon emissions and evaluates the changes in carbon mitigation of residential buildings across 30 Chinese provinces from 2000 to 2018. The results indicate that (1) the operational carbon intensity increased in most provinces and the average annual growth rate across the 30 provinces was 4.2%; (2) from 2001 to 2018, North China and Northeast China had the highest average annual carbon mitigation intensity, at 602.7 and 376.9 kg of carbon dioxide per household, respectively. However, Northwest China had the highest carbon mitigation efficiency, with a carbon mitigation rate of 23.5%; and (3) in most cases, the total carbon mitigations of the operational residential buildings assessed at the provincial scale higher than those assessed nationwide, with a difference of 14.4 million tons of carbon dioxide on average. In addition, this study reviewed the energy efficiency codes for residential buildings and summarized effective energy efficiency measures. Overall, this study fills a gap in our understanding of carbon mitigation tools and provides a reference for the evaluation of historical carbon mitigation effects in the operation of residential buildings.

1. Introduction

The energy consumption of buildings accounts for 36% of global final energy consumption and nearly 40% of carbon emissions [1]. While a building is in use, the energy input from outside (building operation carbon) includes the energy consumption for maintaining the environment of the building (e.g., heating, cooling, ventilation, air conditioning, and lighting) and the energy consumption for various activities within the building (e.g., office usage, home appliances, elevators, and domestic hot water) [2]. In 2019, the indirect and direct emissions from commercial heating and building electricity rose to 10 gigatons of carbon dioxide, the highest level on record [3]. Evidence shows that the building sector produces large carbon emissions, which indicates a significant opportunity for carbon mitigation [4]. In 2019, carbon emissions from the operational buildings reached 2.1 billion tons, accounting for 21.6% of carbon emissions in China [5]. Within this figure, the energy consumption of residential buildings operations accounted for 59.8% and the carbon emissions accounted for 59.0% of all building operations, which demonstrates an urgent need for carbon mitigation in residential buildings [6]. The significant carbon mitigation potential remains untapped because of the continuing use of fossil fuels, the absence of effective energy efficiency policies, and the underinvestment in sustainable buildings. Therefore, to achieve the goal of carbon neutrality, it is essential to explore changes in carbon emissions from the operational residential buildings.

China is the biggest carbon emitter in the world. The annual growth rate of building energy demand in China is high, at about 2.8% on average, which increases the pressure to reduce emissions [7]. There is evidence that China’s carbon emissions will reach 10.3 gigatons in 2025 and decline sharply after 2035, reaching net zero in 2060, which shows great emission reduction potential [8]. There are significant differences in the socioeconomic and climatic conditions in different regions of China, resulting in regional inequalities in low-carbon development [9,10]. Therefore, it is necessary to study the historical carbon mitigation in different provinces and to explore the current status of carbon mitigation and to dissect the reasons for the changes. The key to reducing regional inequalities in low-carbon development is the application of energy efficiency technologies and the mastering of energy intensity changes. The main reason for the differences in the environmental Kuznets curve between provinces is that energy consumption is still a factor that promotes the economic development of most Chinese provinces, but also poses the problem of growth in carbon emissions [11,12]. Although there has been some speculation about the current imbalance in low carbon regional development, to date there are few methods for the further evaluation of carbon mitigation of residential building operations in China’s provinces, mainly because detailed carbon mitigation data for residential buildings have not been published, despite the completion of inspections and measurements of the emission reduction and energy efficiency of buildings. Some studies have tried to evaluate the changes in carbon emissions from Chinese commercial buildings [13,14], but no study assessing the provincial carbon mitigations for Chinese residential building operations has yet been carried out. In addition, three urgent questions associated with reducing carbon emissions in the operational residential buildings in China have not been answered:

• How have the operational carbon emissions changed at the provincial scale in recent years?
• What are the characteristics, similarities, and differences of China’s national and provincial carbon emission intensity?
• How can the operation of residential buildings be decarbonized in response to the carbon neutrality goal?

These questions are significantly related to the key challenges of carbon mitigation in residential buildings, such as how to ensure a balance between efficiency and fairness when the country allocates regional emission reduction targets, and how to formulate targeted policies for reducing building energy consumption [15]. To address the above key questions, this study develops a study of the historical carbon emission mitigation of residential building operations. Using the most recent edition of the China Building Energy and Emission Database (CBEED), this study assesses carbon mitigation at the provincial scale (2000–2018) from the three perspectives of carbon mitigation intensity, carbon mitigation, and carbon mitigation efficiency. Additionally, this study attempts for the first time to examine historical carbon mitigation in the operation of residential buildings at the provincial scale. Most previous studies have concentrated on a single nation [16] or a single sector [17]. The major contributions of this study are to quantify the provincial carbon mitigation effects of residential building operations, propose an assessment model, identify the historical carbon emission reduction benchmarks, and provide a reference for deep decarbonization.

The rest of this study is divided into several sections: A literature overview of existing studies is presented in Section 2. In Section 3, the research process, approach, and data sources are introduced. Section 4 sets out the results. Section 4.1 details the decomposition analysis of the influencing factors driving change in carbon intensity and Section 4.2 further evaluates carbon mitigation under three emission scales. Section 5 lists the comparative variations of carbon mitigation intensity (Section 5.1) and carbon mitigation (Section 5.2) at a provincial and national scale. Section 5.3 summarizes China’s residential building codes and provides some measurement suggestions. At the end of this study, the main findings and future work direction are proposed.

2. Literature Review

Accurate and rigorous sources of carbon emission data are a prerequisite for the assessment of carbon emission intensity. Generally, countries have relevant agencies to evaluate and publish the data. For example, the Energy Information Administration (EIA) of the United States conducts and publishes an energy survey on energy consumption and housing features at a national scale [18]. Carbon emission data for buildings began to be collected in China in 2005, although only a small amount of official data has been made available. Currently, the sources of carbon emission data for China’s building sector are mainly China’s construction energy model and CBEED. The CBEED database is issued by the China Building Energy Efficiency Association, which uses a top-down methodology in conjunction with the China Energy Statistics Yearbook to collect time-series data at the provincial scale. In comparison, the model established by Tsinghua University adopts a bottom-up approach to collect national energy consumption data through large-scale monitoring and statistics [19]. Although there are differences between the two methods in terms of collection method and scope, the national data obtained are similar.

The logarithmic mean Divisia index (LMDI) method has been extensively employed to decompose and analyze carbon emission data. Torvanger [20] first decomposed the drivers of carbon mitigations in energy-related manufacturing industries in nine organizations for economic co-operation and development over a 15-year period using the Divisa index approach. In 2004, Ang [21] compared different decomposition analysis methods and found LMDI to be the best, and further introduced the theory of LMDI as well as specific guidance for its use in a subsequent study [22]. Zhang, et al. [23] used decomposition analysis to build an assessment framework for carbon emission reductions in commercial buildings in China and the United States (US), indicating that the historical annual carbon reduction intensity in China from 2001 to 2018 was 9.8 kgCO₂/m², while in the U.S. was 17.7 kgCO₂/m². At the national level, the LMDI method was used to measure the drivers of carbon emissions in Japan [24], India [25], China [26], and Malaysia [27]. With regard to industry, the LMDI approach was used to analyze carbon emissions in the electricity [28], manufacturing [29], and building sectors [30].

In view of the existing studies, there are two problems that need to be tackled in the assessment of carbon mitigation in the operational residential buildings in China.

So far, changes in carbon intensity and their determinants have not been examined at the provincial scale. Economic development, social conditions, and the climatic environment of the various provinces in China can affect the changes in carbon intensity in residential building operations, which will produce different results using the LMDI approach, further affecting the assessment of carbon mitigation [31,32]. However, because of the absence of reliable carbon emission and floor area data at the provincial scale, most studies only deal with carbon mitigations from residential buildings at the national scale.

The existing carbon mitigation assessment system is primarily based on absolute values of changes in carbon emissions. In most current studies at the national scale, the assessment of the carbon mitigation effect is mainly focused on comparing the absolute values of carbon mitigation in different years, mainly because carbon mitigation changes are more obvious compared with other variables [33].

However, considering that the goal of reducing carbon emissions from the operation of residential buildings is to reduce carbon intensity, a carbon intensity indicator needs to be introduced to evaluate carbon mitigation per household. Moreover, provinces typically use total carbon emissions as the primary basis for setting carbon mitigation targets. To eliminate the influence of total carbon emissions of carbon mitigation, carbon mitigation efficiency is introduced to evaluate the carbon mitigation effect across the provinces and to clarify the future carbon mitigation potential more objectively.

The main aims of this study can therefore be summarized as follows:

• To make the first attempt to assess carbon mitigation in the operation of residential buildings in 30 provinces of China at the provincial scale. By obtaining the latest data on total energy consumption, total carbon emission, total building area, and household sizes of residential buildings, the changes in provincial carbon intensity are measured. The carbon intensity is decomposed and analyzed to identify six drivers and quantify the contributions of various drivers to the carbon mitigation of residential buildings
• To establish a provincial carbon mitigation assessment framework. Based on carbon mitigation intensity, total carbon mitigations, and the carbon mitigation efficiency of residential building operations, this study establishes an assessment framework for carbon mitigation at the provincial scale for the first time. Regions were identified according to the geographical locations of different provinces, and the effects of provincial carbon mitigation are assessed at different scales. The provincial carbon emission mitigation results are analyzed and compared with the national assessment results based on carbon intensity changes and annual carbon mitigation changes. In addition, this study reviews the carbon mitigation strategies of Chinese residential buildings, proposes measures to promote carbon mitigation in residential buildings, and provides theoretical references for provincial carbon mitigation efforts.

3. Methods and Materials

Section 3.1 introduces an evaluation model to identify drivers for inhibiting the growth of carbon emissions which will then become potential drivers of carbon mitigation. The carbon mitigation intensity, carbon mitigation, and carbon mitigation efficiency at the provincial scale are then defined in Section 3.2. Section 3.3 provides a descriptive analysis of the research data.

3.1. Residential Building Emission Characterization

An assessment model combined with the extended Kaya identity for carbon mitigation in the operation of residential buildings has been widely used in existing studies [34,35]. This study is the first to use carbon emission per household to distinguish the carbon intensity of residential building operations at the provincial scale. As demonstrated in Equations (1) and (2), the model can quantitatively explain the influence of population, economy, and technology for the environment, and the Kaya identity is an extension form [36].

$$I=P·A·\mathrm{T}$$

(1)

$$C{O}_2=P·\frac{E}{GDP }·\frac{C{O}_2}{E}·\frac{GDP}{P}$$

(2)

Kaya identity is a residual-free decomposition approach that can be scaled to the characteristics of carbon emissions and household economic development in each province, allowing a comprehensive test of the specific impact of emission drivers. This study selected and illustrated six drivers for carbon mitigation in residential building operations. The explanation and quantitative relationships of the six drivers are shown in Equations (3)–(5) and

Table 1. Explanation of six carbon intensity drivers for residential building operations.

Variables	Symbol	Unit	Expression
Carbon emissions	$C$	Mega tons of carbon dioxide (MtCO2)	-
Energy consumption	$E$	Mega tons of standard coal equivalent (Mtce)	-
Gross floor space	$F$	Million square meters (m2)	-
Household size	$H$	Household	-
Total income	$I$	Billion Chinese yuan (CNY)	-
Population	P	Million persons	-
Housing price	$P_r$	CNY/m2	-
Average household size	$p$	Person/household	$p=\frac{P}{H}$
Housing price to income ratio	r	-	$r=\frac{F·P_r}{I}$
Income per capita	$i$	CNY/person	$i=\frac{I}{P}$
Housing purchasing power	$d$	m2/CNY	$d=\frac{1}{P_r}$
Energy intensity	$e$	kgce/m2	$e=\frac{E}{F}$
Emission factor	$K$	kgCO2/kgce	$K=\frac{C}{E}$

.

The carbon emissions of the operational residential buildings can be determined as:

$$C=P·\frac{F·P_r}{I}·\frac{I}{P}·\frac{1}{P_r}·\frac{E}{F}·\frac{C}{E}$$

(3)

Accordingly, carbon intensity can be acquired as Equation (3) divided by the total number of households ($H$):

$$c=\frac{C}{H}=\frac{P}{H}·\frac{F·P_r}{I}·\frac{I}{P}·\frac{1}{P_r}·\frac{E}{F}·\frac{C}{E}$$

(4)

$$\mathrm{refined} \mathrm{as} c=p·r·i·d·e·\mathrm{K}$$

(5)

3.2. Assessment of Historical Carbon Mitigations

To better assess the historical carbon mitigation, this study is the first to investigate the carbon emission in residential building operations from carbon mitigation intensity, carbon mitigations and carbon mitigation efficiency. Carbon intensity can be determined by six drivers decomposed using the extended form of Kaya identity, and the specific relationship is illustrated in Equation (5). To better identify the influence of various drivers on carbon emissions, this study used the LMDI approach to further analyze the drivers. Due to the lack of residual values after decomposition and the homology of the results, LMDI has been commonly used in existing studies. Based on Equation (5), the specific decomposition of internal carbon intensity can be proposed as follows:

$$\Delta c{|}_{0\to T}=c{|}_T-c{|}_0=\left(\Delta c_p+\Delta c_r+\Delta c_i+\Delta c_d+\Delta c_e+\Delta c_k\right){|}_{0\to T}$$

(6)

With regard to the drivers illustrated in Equation (6):

$$\Delta c_p=L\left(c{|}_T, c{|}_0\right)·ln\left(\frac{p{|}_T}{p{|}_0}\right) = L\left(c{|}_T, c{|}_0\right)·ln\left(\frac{P{|}_T\times H{|}_0}{P{|}_0\times H{|}_T}\right)$$

(7)

$$\Delta c_r=L\left(c{|}_T, c{|}_0\right)·ln\left(\frac{r{|}_T}{r{|}_0}\right) = L\left(c{|}_T, c{|}_0\right)·ln\left(\frac{F{|}_T\times I{|}_0}{F{|}_0\times I{|}_T}\right)·ln\left(\frac{P_r{|}_T\times I{|}_0}{P_r{|}_0\times I{|}_T}\right)$$

(8)

$$\Delta c_i=L\left(c{|}_T, c{|}_0\right)·ln\left(\frac{i{|}_T}{i{|}_0}\right) = L\left(c{|}_T, c{|}_0\right)·ln\left(\frac{I{|}_T\times P{|}_0}{I{|}_0\times P{|}_T}\right)$$

(9)

$$\Delta c_d=L\left(c{|}_T, c{|}_0\right)·ln\left(\frac{d{|}_T}{d{|}_0}\right) = L\left(c{|}_T, c{|}_0\right)·ln\left(\frac{P_r{|}_0}{P_r{|}_T}\right)$$

(10)

$$\Delta c_e=L\left(c{|}_T, c{|}_0\right)·ln\left(\frac{e{|}_T}{e{|}_0}\right) = L\left(c{|}_T, c{|}_0\right)·ln\left(\frac{E{|}_T\times F{|}_0}{E{|}_0\times F{|}_T}\right)$$

(11)

$$\Delta c_K=L\left(c{|}_T, c{|}_0\right)·ln\left(\frac{K{|}_T}{K{|}_0}\right) = L\left(c{|}_T, c{|}_0\right)·ln\left(\frac{C{|}_T\times E{|}_0}{C{|}_0\times E{|}_T}\right)$$

(12)

$$\mathit{thereinto}L\left(c{|}_T, c{|}_0\right)=\{ \begin{array}{c}\frac{c{|}_T-c{|}_0}{ln\left(c{|}_T\right)-ln\left(c{|}_0\right)}, c{|}_T\ne c{|}_0 (c{|}_T>0, c{|}_0>0)\\ 0 , c{|}_T=c{|}_0 (c{|}_T>0, c{|}_0>0)\end{array}$$

(13)

Finally, the carbon emissions generated by the drivers that inhibit growth constitute the carbon mitigation intensity, as shown in Equation (14):

$$C{M}_i{|}_{0\to T}=-{\displaystyle \sum }\left(\Delta c_n{|}_{0\to T}\right)$$

(14)

$$\mathit{where}\Delta c_n \in \left(\Delta c_p, \Delta c_r, \Delta c_i, \Delta c_d, \Delta c_e, \Delta c_k\right), \Delta c_n{|}_{0\to T}<0$$

The total carbon mitigations ($CM$) during the study period can be obtained by multiplying the carbon mitigation intensity and the number of households:

$$CM{|}_{0\to T}=C{M}_i{|}_{0\to T}·H$$

(15)

In addition, to more intuitively show the potential of carbon mitigations in terms of total emissions, the carbon mitigation efficiency of the operational residential buildings is shown as the mitigation ratio in Equation (16):

$$CME{|}_{0\to T}=\frac{CM{|}_{0\to T}}{C{|}_{0\to T}}$$

(16)

where $CM$ is equal to that in Equation (15), because the carbon mitigations generated during the study period for the operational residential buildings specify the total carbon emissions for the province during the same period.

3.3. Dataset

Data from 30 provinces were obtained from CBEED (http://www.cbeed.cn/, accessed on 27 June 2022), including total energy consumption, gross floor space, and total carbon emissions of provincial residential buildings from 2000 to 2018. The study area included 30 provinces in China (see Appendix A). The total population, household size, and housing prices were obtained from the National Bureau of Statistics of the People’s Republic of China. It should be noted that the household income per capita was calculated using urban and rural incomes. Appendix B summarizes the descriptive statistics of the variables and the review of carbon mitigation strategies for residential buildings.

4. Results

4.1. Changes of the Operational Carbon Intensity of Residential Building

Figure 1 uses the LMDI approach (see Equations (6)–(13)) to decompose the drivers of carbon emission in residential buildings across 30 Chinese provinces, and to evaluate the changes in carbon intensity from 2000 to 2018. As illustrated in the decomposition results, the carbon intensity of most provinces increased, and the annual growth rate of the 30 provinces was 4.2%. Ningxia was the only province that experienced carbon intensity decline during the study period. It can clearly be seen that carbon intensity is affected by energy policy with regard to the changes in carbon intensity that occurred around 2009. Twenty provinces had a lower carbon intensity growth rate from 2009 to 2018 than from 2000 to 2009, and eight of them (mainly economically developed provinces) showed negative growth in 2009–2018 (e.g., Beijing: −7.6%, Tianjin: −2.6%, Shanghai: −2.5%). The variation of carbon intensity is also influenced by climate zones. Generally, average carbon intensity gradually decreases from severe cold to warm climatic zones [37]. However, it is worth mentioning that according to the observation results, the average carbon intensity does not decrease precisely in line with the order of climate zones. It was observed that cold zones have the highest carbon intensity, followed by severe cold, hot summer and cold winter, warm, and hot summer and warm winter zones. In addition, there are some provinces in the same climate category that have significantly higher carbon intensity than other provinces in the category, such as Beijing in the cold zone, Shanghai in the hot summer and cold winter zone, and Guangdong in the hot summer and warm winter zone.

In terms of emission drivers, from 2000 to 2018, income per capita was the biggest contributor to the growth in the carbon intensity of residential buildings, followed by housing price to income ratio, whereas housing purchasing power was the largest negative contributor. Taking Guangdong as an example, from 2000 to 2009 and from 2009 to 2018, income per capita accounted for 110.0% and 88.6% of the change in total carbon intensity, respectively. Similarly, the housing price income ratio accounted for 22.4% before 2009 and 30.1% after 2009. Family housing purchasing power was the most negative contributor, accounting for −104.0% and −78.6% in the two study periods, respectively. The effects of emission factors and average household size on changes in carbon intensity were mainly manifested as suppressed growth. In contrast, the effects of energy intensity on the operational carbon intensity were not fixed. For example, the contribution rate of energy intensity in most provinces was positive before 2009 but negative after 2009.

4.2. Carbon Mitigations of Residential Buildings

With regard to the results presented in Section 4.1, the carbon mitigation intensity can be acquired using Equation (14) and the carbon mitigation intensities of each province in different years are shown in Figure 2.

North China and Northeast China showed high carbon mitigation intensity during the study period, with an annual carbon mitigation intensity of 602.7 kg of carbon dioxide per household (kgCO₂/household) and 376.9 kgCO₂/household on average, respectively.

Before 2010, high value clusters were mainly located in North China and Northeast China. After 2010, carbon mitigation intensity began to decrease and the total carbon mitigations between provinces were closer. From 2017, carbon mitigation intensity began to pick up and even exceed the peak that occurred around 2009. Around 2018, the high-value clusters of carbon mitigation intensity were mostly concentrated in North and Southwest China. For example, in 2018, Shanxi and Guizhou reached peaks of 1557.5 kgCO₂/household and 1606.9 kgCO₂/household, respectively.

The carbon mitigations for each province can be obtained through carbon mitigation intensity and household size (see Equation (15)). As shown in Figure 3, the high-value carbon mitigations were concentrated in North China and some parts of South China (Guangdong and Hunan). The average annual carbon mitigation for each province was 4.4 million tons of carbon dioxide (MtCO₂) from 2000 to 2018. Before 2010, the carbon mitigations for each province increased gradually and peaked slightly in 2010. In 2018, the high-value clusters of carbon emission reductions were mainly concentrated in North China, with some in East China (Shandong, Jiangsu, and Shanghai), Southwest China (Guizhou, Yunnan, and Chongqing), and South China (Guangdong). Of these, Shanxi had the highest annual carbon mitigation of 19.4 MtCO₂ from 2000 to 2018.

To deeply explore the future carbon mitigation potential, carbon mitigation efficiency was included in the evaluation framework, expressed as the proportion of carbon mitigations in the total carbon emissions from residential buildings for the whole province (see Equation (16)). The overall carbon mitigations and corresponding carbon mitigation efficiency of each province is shown in Figure 4.

Northwest China, which includes most of the poorest regions of China, had the highest carbon mitigation efficiency, with a region-wide average carbon mitigation efficiency of 23.5%. Although the Northwest had the highest carbon mitigation efficiency, the mitigation was the smallest of all regions, with an average carbon mitigation of 46.6 MtCO₂. In contrast, North China and Central China had relatively higher average carbon mitigations of 104.1 MtCO₂ and 99.2 MtCO₂, respectively, but the carbon mitigation efficiency was low. For example, Hebei’s carbon mitigation was 151.7 MtCO₂ during the study period, exceeding 93.3% of the samples, but Hebei’s carbon mitigation efficiency was only 15.4%, which was the lowest among all the samples. It can be clearly observed that compared with other provinces, there are significant obstacles to further promoting carbon mitigation in Hebei. In Central China, a similar situation was found in Hunan and Henan.

In summary, the results set out above demonstrate the changes in carbon intensity and evaluate in detail the carbon mitigation effect at the provincial scale, which addresses question 1 in Section 1.

5. Discussion

This study proposes an assessment framework through the examination of changes in the carbon emissions of residential buildings. To validate the robustness of the assessment framework, this section compares the provincial and national carbon mitigation results. Section 5.1 discusses the differences in carbon mitigation intensity changes at the provincial and national scales. Differences in annual carbon mitigation levels are investigated in Section 5.2. Finally, Section 5.3 examines improvements in China’s residential buildings at the provincial scale faced with carbon neutrality targets and reviews related policies.

5.1. Differences of Carbon Mitigation Intensity at National and Provincial Scales

The changes in the carbon intensity of residential buildings at the national level and drivers from 2000 to 2018 were explored through the analysis of national residential building operational carbon intensity data.

It can be noted from Figure 5 that the change trends of carbon intensity at provincial and national scales are similar. With regard to the provincial scale, an annual rate of increase of 4.2% can be seen in Figure 1, which is relatively low compared with the national scale of 5.7%. Compared to 2000–2009, the carbon intensity at provincial and national scales continued to decline during 2009–2018, and the growth rate of carbon intensity in the provinces dropped to 2.6% during 2009–2018. In addition, the national carbon mitigation intensity was 1911.5 kgCO₂/household on average, which is higher than the average carbon intensity of 19 provinces.

The contribution of the drivers at national and provincial scales is also largely the same. Income per capita was still the biggest contributor to the increase in carbon intensity, accounting for 155.8% and 113.1% in the two study periods. Therefore, the positive effect of income per capita on the carbon emissions of residential buildings can be concluded from the observations at provincial and national scales. Consistent with the results at the provincial scale, the decline in housing purchasing power at the national scale significantly inhibited growth in carbon intensity. As shown in Figure 1, decreases in housing purchasing power in Tianjin and Shaanxi during the study period offset the growth in carbon intensity because of the growth in income per capita and the house price to income ratio.

5.2. Difference of Annual Carbon Mitigation at the National and Provincial Scales

Compared with the national scale, the annual carbon mitigation of residential buildings at the provincial scale adopts a smaller calculation unit, which can provide more detailed and accurate data. Figure 6 shows the absolute difference in annual carbon mitigation at the two calculation scales between 2001 and 2018.

It can be noted that the sum of provincial carbon mitigation is larger than the national scale in 13 out of 18 years, with a difference of 14.4 MtCO₂ on average. It can be observed that the total carbon mitigations evaluated at the sum of provincial carbon mitigations are higher, peaking at 233.2 MtCO₂ in 2018. This variation between provincial carbon mitigations and national carbon mitigations arises mainly from differences in the raw calculated data. The data at both national and provincial scales are obtained from the energy balance sheets of China’s official statistics [38]. However, as the provinces employed different accounting systems, the carbon emissions at the national scale cannot be obtained from the summation of data at the provincial scale.

Additionally, there was a significant difference in carbon mitigations at the two scales in 2013, with 201.0 MtCO₂ at the provincial scale and 111.9 MtCO₂ at the national scale, a difference of 89.1 MtCO₂, which is larger than the average difference. An abnormal value of building energy consumption leads to a high building emission reduction rate around 2013. In the studies by Ma, et al. [39], it can also be clearly observed that the abnormal data on the national building carbon emission curve around 2013 is mainly attributable to the low carbon mitigation value counted at the national scale. In general, because of doubts about the reliability of data sources at the national scale, it is essential to further verify the accuracy of carbon mitigations at the national scale by using the sum of provincial carbon mitigations.

Overall, the discussion above illustrates the differences in carbon mitigation intensity and carbon mitigations at national and provincial scales, which comprehensively answers question 2 in Section 1.

5.3. Carbon Mitigation Strategies for Residential Buildings

As well as quantifying the effects of carbon mitigation, a review of energy policies for residential buildings is also significant to summarize current low-carbon practices [40]. An effective and efficient energy efficiency policy system is critical to achieving carbon mitigation goals in the building sector [41,42]. In addition, operational carbon emissions are closely related to building energy consumption [43]. With regard to the carbon mitigation practices implemented by the government, China has developed three main work pathways to promote the energy efficiency of residential buildings (see Figure 7). First, the energy efficiency standards for residential buildings have been steadily increasing. During the 12th Five-Year Plan period, the proportion of new-build residential buildings in China implementing mandatory energy efficiency standards reached 100%, with a cumulative increase of 7 billion square meters of energy efficient floor space, and the proportion of energy efficient buildings was more than 40%. Even in some severe cold and cold areas such as Beijing and Shandong, the mandatory 75% energy efficiency goal in new residential buildings has been fully implemented [44]. Second, the energy efficiency renovation of residential buildings has been comprehensively promoted. By the end of the 12th Five-Year Plan, the energy efficiency retrofit project for existing residential buildings in the northern heating areas has been completed, with an annual energy saving of 6.5 Mega tons of standard coal equivalent [45]. In hot summer and cold winter areas, projects for the energy efficiency renovation of existing residential buildings covered 71 million square meters. Third, new breakthroughs have been made in green residential buildings [46]. The Green Building Action Plan and the national New Urbanization Plan (2014–2020) have effectively promoted the development of green buildings. From the perspective of green building identification projects, during the 12th Five-Year Plan period, 4071 projects (470 million m²) obtained green building evaluation identification. In terms of the standard system, standard documents such as energy efficiency design standards for rural residential buildings and energy efficiency technical guidelines for rural housing in severe and cold areas have been promulgated and implemented successively, and the standard framework for energy efficiency for rural buildings and green buildings has started to take shape.

5.3.1. Carry Out Construction Emission Reduction according to Local Conditions and Strengthen Collaborative Governance among Provinces

In the general context of national emission reduction, the emission reduction responsibilities of various regions are different, so their emission reduction targets and approaches are also different. Existing studies have shown that there are certain differences within and between different regions, and the differences between regions are greater [47]. For provincial emission reduction, it has been suggested that practices are first implemented within a region and then between regions [48], that is, the advanced experience in this study can be used as a reference for each province before decisions are made. This approach draws on the experience of provinces outside the region, reflecting the “easy first, then difficult” mode of work. At a regional level, advances can be made by strengthening collaborative governance between provinces, learning from benchmarking, improving communication and exchange, jointly learning about and using emission mitigation strategies, and then making efficient use of the various resources.

5.3.2. Increase Investment in Research and Development and Promote Higher Quality Economic Development

There is a relationship between carbon emissions from buildings and economic scale in China. Economic scale is one of the factors affecting the carbon emissions from buildings [49]. During the past 18 years, the economic scale effect in low-emission and high-efficiency areas has had a dampening effect, indicating that the quality of regional economic development is low and the reduction in economic scale will have obvious emission mitigation effects [50]. Therefore, it is necessary to enhance the quality of regional economic development to control carbon emissions from buildings. Decision-makers should pay more attention to improving the quality of economic development, and research and development is a guaranteed means to promote high-quality economic development [51]. In regions with low emissions and low efficiency, the level of research and development and the quality of economic development is low. Therefore, there is an urgent need to increase investment in research and development to promote technological development and provide support for higher quality economic development.

5.3.3. Strengthen the Technical Capacity of Building Energy Efficiency and Carbon Mitigation and Improve the Utilization Efficiency of Energy in Buildings

Building energy consumption per floor space has a strong inhibitory effect on carbon emissions. Thus, carbon emissions from buildings can be effectively reduced by decreasing building energy consumption per floor space. With the development and progress of society, both in terms of economic development and urbanization, the building area gradually increases, and there is a general improvement in building energy consumption [52]. Through the application of building carbon mitigation technology, the growth rate of building energy consumption and area can be effectively reduced, thus creating the effect of reducing building energy consumption per unit area [53]. Improvements in emission reduction technology bring improvements in energy efficiency. People can reduce their energy use on the basis of ensuring the energy use effect. There are many ways to strengthen the technical capacity of carbon mitigation in buildings, such as by increasing the proportion of green buildings [54], establishing mandatory standards for green buildings [55], improving the application of energy efficiency technologies [56], formulating incentive policies for relevant enterprises and units using relevant technologies [57], and by constantly promoting the construction and development of the energy efficiency technology market.

5.3.4. Continuously Optimize the Energy Consumption Structure of Buildings and Increase the Proportion of Clean Energy

Previous studies have shown that the regional comprehensive carbon emission factor has a particularly obvious inhibitory effect on building carbon emissions. Therefore, to achieve greater carbon mitigation, it is necessary to reduce the comprehensive carbon emission factor [58]. In recent years, work carried out in China has made the inhibitory effect of comprehensive carbon emission factors in different regions increasingly obvious. As decarbonization work has developed in recent years, the conversion from coal to electricity and from coal to gas has achieved remarkable results, and the proportion of clean energy in China has greatly increased. The carbon emission per unit of clean energy consumption is relatively small, so it has a significant effect on building emission reductions [59]. The carbon emission from buildings defined in this study is the carbon emission in the operation stage of residential buildings. The types of energy used in the building operation stage are diverse, and the use structure across regions is not exactly the same. Thus, the regions should continue to optimize the building energy consumption structure [60], increase the use of clean energy, and realize sustainable development.

6. Conclusions

This study decomposed the factors influencing residential building carbon emissions in 30 provinces of China and evaluated the changes in historical carbon intensity caused by residential building operations from 2000 to 2018. The carbon mitigation effects of residential building operations were then explored from the following perspectives: carbon mitigation intensity, carbon mitigations, and carbon mitigation efficiency. Furthermore, the results were expanded and discussed at the provincial and national scales. Finally, this study reviewed the low-carbon development strategies for residential buildings and summarized effective energy efficiency measures to provide references for the further achievement of the goal of carbon neutrality in the operational residential buildings.

6.1. Main Findings

• The carbon intensity of provincial residential building operations continued to rise but the growth rate gradually slowed from 2000 to 2018, and the operating carbon intensity increased by an average of 4.2% per year at the provincial scale. Twenty provinces experienced a lower growth rate during the period 2009–2018 compared with 2000–2009, and eight of them (mainly economically developed provinces) showed negative growth in 2009–2018. Household income per capita was identified as the largest contributor to the operational carbon intensity of residential buildings, followed by the housing price to income ratio, and the household housing purchase index was considered to be the largest negative contributor.
• From 2001 to 2018, North China had the highest carbon mitigation intensity and total carbon mitigations, with average values of 602.7 kgCO₂/household and 5.8 MtCO₂, respectively. From the perspective of carbon mitigation intensity, carbon mitigations and carbon mitigation efficiency of the operational residential buildings, there were obvious regional differences among the provinces. In addition, the significance of carbon mitigation in residential buildings was identified, with the highest carbon mitigation rate shown at 23.5%.
• In most cases, the total carbon mitigations of the operational residential buildings evaluated at the provincial scale was larger than that observed at the national scale, reaching a peak of 233.2 MtCO₂ in 2018. Although the impact of the drivers was observed to be consistent at both national and provincial scales, in terms of annual carbon mitigations, the values evaluated at the provincial scale were larger than at the national scale in most cases, with an average discrepancy of 14.4 MtCO₂. To improve the level of energy efficiency, decision-makers should formulate regional carbon mitigation plans and policies according to local conditions, which should include building an energy monitoring platform, strengthening the technical capacity of energy efficiency and carbon mitigation, and continuously optimizing the structure of building energy consumption.

6.2. Upcoming Studies

Although this study closes the research gap regarding the factors influencing changes in carbon emissions and carbon mitigation evaluation in residential buildings, further expansion and improvement can be pursued in terms of basic emissions data, research tools, and research scope. First, the data source for the quantitative analysis in this study was the China Building Energy and Emissions Database developed by our team, which does not include the decomposition of data on building energy consumption and emission in end use (heating, refrigeration, lighting, cooking, hot water, household appliances, and others). The further study of the carbon emission characteristics generated by terminal itemized energy consumption will be significant. Second, the tools for assessment of the carbon mitigation potential of the building sector should continue to be improved in the future. This study successfully demonstrated the carbon mitigation levels under three different historical emission scales. The approach can be applied to the assessment of the carbon mitigation potential of future residential buildings. In the future, the decoupling of energy consumption and economic development can be analyzed in detail to assist China’s building sector in achievement of the carbon neutral target. In addition, the research scope of historical carbon mitigation can be expanded. The level of carbon mitigations for buildings in other high-carbon-emitting economies (such as the United States and Japan) and international alliance organizations are also worth studying. An assessment to demonstrate the cost-effective emission reduction potential of the global building sector will help the global transition of buildings to low carbon in the carbon neutral era.