Worldwide, the building sector is considered to be a major contributor to global carbon emissions, requiring and consuming vast amounts of energy [1
]. Fruitful building energy efficiency (BEE) work is a prerequisite for establishing sustainable urbanization. China has become the world’s main producer of carbon emissions, with the building sector of the country producing the second most carbon emissions nationwide. Accordingly, the BEE work in China is being confronted by important challenges [1
]. As a typical part of building carbon emissions (BCE), China’s public building carbon emissions (CPBCE) accounted for approximately 40% of the BCE in China in 2015 [5
]. Several studies have forecasted that CPBCE would exceed 1,200 million tons of carbon dioxide (MtCO2
) by the 2030s if the Chinese government failed to immediately implement a carbon emission control strategy (CECS) relevant to public buildings [2
]. Obviously, this would increase CPBCE significantly and hamper the progress of sustainable urbanization in China [2
]. As the potential for carbon emission reduction (CER) in public buildings in China is greater than in residential buildings, the CECS relevant to buildings must be adopted nationwide by the public building sector [14
]. This implies that the carbon emission reduction in China’s public buildings (CERCPB) must be assessed to ensure that the public buildings’ CECS is being implemented [8
The authoritative quantification of CPBCE in China has fallen behind substantially, thereby severely influencing the assessment of CERCPB, as such quantification requires dependable and specific CPBCE data. In the statistics system of China relevant to carbon emissions, BCE has been allocated to an autonomous carbon emissions department, but the carbon emission data are dispersed in various social divisions [6
]. Consequently, at present, authoritative BCE data are still lacking. However, since 2007, sustainable and comparatively systematic calculation models have been established by several researchers in China to compensate for the lack of BCE data (as shown in Figure 1
). Tsinghua University constructed the China Building Energy Model (CBEM) in 2007 [15
]. This was the first bottom-up-type model in China to evaluate BCE data nationwide, indicating a BCE value of 2246.40 MtCO2
, accounting for 20.00% of the total national carbon emissions (TNCE) in 2015. The CPBCE (excluding the heating-related BCE in northern China) amounted to 676 MtCO2
, accounting for 30.09% of the BCE [7
]. Chongqing University established the China Macroscopic Building Energy Consumption Statistical System (CMBECSS) in 2010 [16
]. The original data sources of the CMBECSS were the energy balance sheets of the China Energy Statistical Yearbook. As a typical bottom-up-type model to evaluate the BCE data nationwide, this system estimated the BCE in China for the period 1985–2009 effectively [2
]. The updated data of the second-generation system (i.e., CMBECSS Ver. 2.0) indicated a BCE value of 2,233.40 MtCO2
, which accounted for 19.98% of the TNCE in 2015 in China. Furthermore, the CPBCE amounted to 863.2 MtCO2
, accounting for 38.65% of the BCE in China [5
Currently, studies on the effective assessment of CERCPB are inadequate, owing to the lack of authoritative CPBCE data. Nevertheless, several relevant published studies have presented feasible methods of assessing the CER in the existing civil building sector of China. These include derivative versions of the Human Impact, Population, Affluence, and Technology (IPAT) equation and index decomposition analysis (IDA), such as the Logarithmic Mean Divisia Index (LMDI), based on the BCE data from the CMBECSS database. With the extended version of the IPAT equation, Ma et al. [2
] proposed the concept of comparable BCE intensity and, thereto established an equation. This equation involves the BCE of China and several of its driving factors to calculate the CER in existing buildings nationally by employing LMDI decomposition analysis for the period from 2001 to 2014. Subsequently, the CMBECSS Ver. 2.0 published a series of updated BCE data for China [17
]. Ma et al. [5
] applied the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model and the additive mode of the LMDI Type I (LMDI-I) method to present an improved calculation model and to further explore the mechanism of the CER in existing civil buildings during 2001 to 2015. Relevant to the residential building sector level, Yan et al. [10
] utilized the IPAT equation and performed LMDI decomposition analysis to estimate the CER in existing residential buildings in China for the period 2001 to 2015.
Two comparatively credible and mature estimation approaches to CPBCE data and several similar methods to calculate the CER in existing civil buildings were introduced by the literature review. In comparison with CBEM, the data issued in the CMBECSS are comparatively credible, as this systematic estimation approach provides sustainable, complete, and comparable time-series data involving CPBCE, which is an essential requirement for assessing CERCPB in 2001–2015. Moreover, these BCE data have been accepted widely by various other studies [2
]. Furthermore, the IPAT equation, including its derivative versions (e.g., the STIRPAT model) and LMDI decomposition analysis are appropriate for identifying and evaluating the different effects on the factors that affect carbon emissions in specific industries [20
]. As CPBCE is typical of carbon emissions, the methods discussed above can be employed also to assess the CER in the public building sector [6
Although several studies have assessed and evaluated effectively the CER in the existing civil and residential buildings, the CERCPB assessment approach remains inadequate because believable and mature data involving CPBCE published by CMBECSS prototype are not available, which further influences the establishment of driving factors affecting CPBCE for assessing the value of CERCPB. Namely, the research gap in this study means that the studies mentioned above have pointed out that research on the individual appraisal of CERCPB could be inadequate, indicating that formulating an effective approach to evaluating CERCPB is a crucial task. In order to support the Chinese government in drafting and applying focused policies and targets for enhancing the CECS of public buildings, CERCPB would need to be assessed quantitatively based on specific CPBCE data. Moreover, the progress of BEE work and sustainable urbanization in China would be supported and promoted by such work. Accordingly, conducting further analysis based on CERCPB assessment results is an important and urgent task.
With respect to the overall contribution and innovation of this study, and in view of the inadequacy of other studies on the effective assessment of CERCPB, the aim of this study is to set up an efficient approach to bridging the research gap relevant to effective CERCPB assessment. This study established the CPBCE equation, considering connected driving factors (i.e., population in China, floor areas of China’s existing public buildings, building service level index of China’s existing public buildings, and the comparable CPBCE intensity) based on the STIRPAT model and through the CPBCE data referenced by the database of CMBECSS Ver. 2.0, thereinto the new driving factor (the comparable CPBCE intensity) which reflects the actual BEE benefit was highlighted for the assessment of the value of CERCPB. An IDA (i.e., LMDI-I decomposition analysis) was performed afterwards to further explore the distinct effects on these driving factors that affect the CPBCE and to appraise the values of the negative contributions of such driving factors (i.e., CERCPB values) over the past fifteen years (2001–2015). Furthermore, an overview of the public BEE work in China from the late 1990s to 2015 is included to demonstrate the internal cause of the considerable accumulation of CERCPB for the period 2001–2015.
The rest of this study is prepared as shown below. Section 2
reveals the theories of the LMDI and STIRPAT approaches; the method to assess CERCPB is then introduced in the same section. Section 3
guides the sources of data. Section 4
indicates and deeply analyzes the outcomes of LMDI-I decomposition analysis, especially the values of CERCPB from 2001 to 2015. Section 5
discusses the development of China’s public BEE work from the late 1990s to 2015, and points out a shortcoming in this study. The conclusions and implications involving energy policies are presented in Section 6
6. Conclusions and Policy Implications
The CERCPB values ought to be assessed to enhance the CECS of public buildings in China and to drive the progress of sustainable urbanization and BEE work. This study presented a method based on the LMDI and STIRPAT approaches for the effective assessment of the CERCPB values for the period 2001 to 2015. An overview of public BEE work in China is included in the study, and a shortcoming is mentioned. The main conclusions of this study are as follows:
The outputs of the LMDI-I decomposition analysis demonstrated that only the contribution of comparable CPBCE intensity to CPBCE was negative during the period 2001–2015, and this contribution indicated the CERCPB value for the period.
The assessment results indicated that CERCPB had accumulated considerably with the rapid development of BEE work in China during 2001–2015. The CERCPB values in 2001–2005, 2006–2010, and 2011–2015 were 69.29, 158.53, and 277.86 MtCO2, respectively. Furthermore, the actual CERCPB values derived by this study were obviously higher than the official planned values for the periods. This illustrates the positive effect of implementing public BEE work in China during 2001–2015.
After summarizing the leading policies and goals of China relevant to public BEE works over the past fifteen years, this study demonstrated that public BEE work achieved significant results during 2001–2015, which could be regarded as a prerequisite to attaining the considerable accumulation of CERCPB over this period.
This study revealed the feasibility of assessing CERCPB values by employing the LMDI and STIRPAT approaches. However, at the data level, although BEE work in China has advanced in various respects, a few shortcomings remain in assessing BCE data nationwide. Relevant to both the residential and public building sectors, detailed and accurate BCE data issued by the government are required to determine precise CER values for these sectors. In addition, these data should be also treated to significant indexes for measuring the progress of BEE work in China. Generally, the Chinese government should make a substantial effort to establish a statistics system relevant to nationwide BCE data. We believe that our study represents a most significant task relevant to the next stage of BEE work in China.
This study focused on public building sector in China. In order to achieve the double reduction (i.e., CERCPB and the reduction of CPBCE intensity) of existing public buildings, the Chinese government should further publish effective and targeted BEE policies for public buildings at the upcoming phase, such as the new versions of Design Standard for Energy Efficiency of Public Buildings (GB 50189), Assessment Standard for Green Buildings (GB/T 50378), and guide rules for energy efficiency retrofit of public buildings. Furthermore, considering that the building sector has an acknowledged role in the TNCE of China, the carbon trading market of the building sector can be further developed to promote the progress of China’s BEE work in the next phase. Given that it is easier to measure and assess the potential and values of CER in public buildings than residential buildings at regional and provincial levels, public buildings should be regarded as playing an important part in effectively promoting the development of the carbon trading market. This effort can further drive the developments of BEE work and sustainable urbanization in China.