# What Is the Amount of China’s Building Floor Space from 1996 to 2014?

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## Abstract

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^{2}in 1996 to 61.3 billion m

^{2}in 2014, increasing more than twice, with an annual growth rate of 4.4% from 1996 to 2014. During 1996–2014, urban residential BFS witnessed the highest annual increase rate (9.3%), while the growth rate for commercial and rural residential BFS was lower: 4.4% and 1.6%, respectively. By comparing with available statistics data, this study finds the model deviations are well below 5%, which indicates the reliability of the proposed method and robustness of the results. The proposed method not only can address the deficiencies of statistic yearbook and overcome the shortages of previous estimation approaches but also can derive more accurate and reliable data. This study lays a sound basis for the following study on building stock and building energy efficiency work.

## 1. Introduction

^{2}, accounting for nearly half of the world’s new building area [24], and 30–40% of the world’s annual output of cement and steel is therefore consumed [25]. About 9% of total energy and 15% of industrial energy would be consumed for generating these building materials [26,27,28]. Besides, the shorter lifespan of the existing buildings in China also leads to the fast growth of construction activities, and this would directly result in massive building wastes [29]. The shorter lifespan of the existing Chinese building stock also leads to the high new construction rate, and this will further result in the material and energy demand. Therefore, accurately measuring the volume of the building floor space is vital for quantifying the material flows.

## 2. Literature Review

## 3. Systematic Analysis of the Deficiencies for the Items Concerning BFS in CSY

#### 3.1. Statistical Caliber Inconsistency

^{2}higher than the former on average, as shown in Figure 2.

#### 3.2. Data Sources Inconsistency

#### 3.3. Data Time Series Inconsistency

## 4. Methodology

#### 4.1. Estimation Method for the Rural Residential Floor Space

#### 4.2. Estimation Method for Urban Residential Floor Space

**Step 1: Correct the URBS from 1996 to 2002**

**Step 2: Estimate the URBS from 2007 to 2014**

#### 4.3. Estimation Method for Rural Commercial Floor Space

^{2}. It is obvious that the data of rural commercial floor space in 2008 is an outlier. Therefore, rural commercial floor space from 2006 to 2015 excluding 2008 can be fitted with a linear regression equation.

#### 4.4. Estimation Method for Urban Commercial Floor Space

**Step 1: Split out the Statistics Data on Urban Commercial Floor Space from 1996 to 2006.**

**Step 2: Correct the Urban Commercial Building Stock from 1996 to 2002**

**Step 3: Estimate the Urban Commercial Building Stock from 2007 to 2014**

## 5. Results and Analysis

^{2}in 1996 to 61.3 billion m

^{2}in 2014, more than twofold increasing during the covered period. The average increase rate of the BFS was 4.4% annually during 1996–2014.

#### 5.1. Characteristics of China’s Building Floor Space

^{2}from 1996 to 2014, and rural residential BFS only grew by 6 billion m

^{2}. This indicated the rapid urbanization of China in recent years. The proportion of the urban residential floor space grew more than two times from 19.2% in 1996 to 43.3% in 2014, whereas the figure for the rural residential floor space declined correspondingly from 65.7% to 40.0% from 1996 to 2014. In 2014, the urban residential BFS overtook the rural residential floor space and accounted for the largest proportion in the BFS in China. During 1996 to 2014, the commercial floor space increased more than tripled, from 4.8 billion m

^{2}to 10.3 billion m

^{2}, although its percentage was only 16%.

#### 5.2. Residential Building Floor Space

^{2}from 1996 to 2014. The average increase rate was 4.3% annually. The per capita residential floor space increased almost two times from 19.5 m

^{2}/person to 37.3 m

^{2}/person over the period of 1996–2014, which is due to the decline of the household size and the growth of the middle class given the higher living standard. This is one of the drivers for the residential BFS dramatic increase. Another reason is the urbanization rate. In line with the data on CSY, the urbanization rate has increased from 30.5% in 1996 to 59.5% in 2019 and is expected up to 80% by 2050 [13]. In order to accommodate the increasing new migrants from the rural area to the urban area, more residential buildings were constructed.

#### 5.3. Commercial Building Floor Space

^{2}during 1996–2014. The average increase rate was 4.4%. The possible reason maybe the increase of the commercial BFS per employee and the growth of the service sector employment share. As shown in Figure 8, the commercial floor space per employee increased from 24.7 m

^{2}/employee to 32.7 m

^{2}/employee over the period of 1996–2014. Based on the data released in CSY, the percentage of the tertiary industry employee witnessed a sharp increase, from 26.0% to 40.6% during the period 1996–2014. The root cause is that a large proportion of migrant workers moved to urban areas with the rapid urbanization process in recent years, and most of them worked in the manufacturing and service sectors, approximately 70% and 22%, respectively, in 2010 [51,52].

## 6. Reliability Analysis and Discussions

#### 6.1. Reliability Analysis for Urban Residential and Commercial Building Floor Space

#### 6.2. Reliability Analysis for Rural Residential and Commercial Building Floor Space

#### 6.3. Discussion

## 7. Conclusions

- This study comprehensively analyzed the existing deficiencies regarding the BFS related statistical indicators in CSY and found that: (1) The statistical caliber on the TUBFS changed over time. (2) The floor space of completed suffers from a heterogeneous data source with great discrepancies. (3) Most of the statistical indicators suffer from incomplete time series data. (4) The data regarding urban commercial floor space cannot be obtained in CSY. (5) For urban residential building area, there is a big gap between the two statistical calibers.
- China BFS were estimated adopting the proposed method, and results indicated that China BFS was 61.3 billion m
^{2}in 2014. Of this, commercial, urban residential and rural residential BFS were 10.3 billion m^{2}, 26.5 billion m^{2}and 24.5 billion m^{2}, respectively. Of China’s BFS in 2014, the urban residential BFS accounted for the largest proportion, 43.3%, like the percentage of the rural residential (40.0%), and the figure for the commercial building was the lowest, 16.8%. - The total floor space in China witnessed an upward trend and increased by 33.2 billion m
^{2}from 1996 to 2014, increasing about two times during this period. The average increase rate of the BFS in the whole period was 4.4% annually. During the period 1996–2014, three types of BFS saw various growth rates: 9.3% for the urban residential BFS and 4.4% and 1.6% for commercial and rural residential BFS, respectively. The rapid growth of the urban residential BFS contributed the most to the increase rate of the total BFS in China throughout the whole period. - By comparing with the statistical data, we found that the deviations were well below 5%. This could indicate the reliability of the results and robustness of the proposed method.

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

- Huo, T.; Li, X.; Cai, W.; Zuo, J.; Jia, F.; Wei, H. Exploring the impact of urbanization on urban building carbon emissions in China: Evidence from a provincial panel data model. Sustain. Cities Soc.
**2020**, 56, 102068. [Google Scholar] [CrossRef] - Mi, Z.; Meng, J.; Guan, D.; Shan, Y.; Song, M.; Wei, Y.-M.; Liu, Z.; Hubacek, K. Chinese CO
_{2}emission flows have reversed since the global financial crisis. Nat. Commun.**2017**, 8, 1–10. [Google Scholar] [CrossRef] [PubMed] - IPCC. IPCC Fifth Assessment Synthesis Report-Climate Change 2014 Synthesis Report. In IPCC Fifth Asses; Report-Climate Change: Geneva, Switzerland, 2014; p. 167. [Google Scholar]
- IEA. World Energy Outlook 2013, International Energy Agency. Available online: https://doi.org/10.1787/20725302 (accessed on 20 June 2013).
- BP. Statistical Review of World Energy. Available online: http://www.indiaenvironmentportal.org.in/content/444050/bp-statistical-review-of-world-energy-2017/ (accessed on 20 April 2017).
- Huo, T.; Ren, H.; Zhang, X.; Cai, W.; Feng, W.; Zhou, N.; Wang, X. China’s energy consumption in the building sector: A Statistical Yearbook-Energy Balance Sheet based splitting method. J. Clean. Prod.
**2018**, 185, 665–679. [Google Scholar] [CrossRef] - Zhou, N.; Lin, J. The reality and future scenarios of commercial building energy consumption in China. Energy Build.
**2008**, 40, 2121–2127. [Google Scholar] [CrossRef] [Green Version] - Sun, C.; Ouyang, X.; Cai, H.; Luo, Z.; Li, A. Household pathway selection of energy consumption during urbanization process in China. Energy Convers. Manag.
**2014**, 84, 295–304. [Google Scholar] [CrossRef] - Du, G.; Sun, C.; Ouyang, X.; Zhang, C. A decomposition analysis of energy-related CO
_{2}emissions in Chinese six high-energy intensive industries. J. Clean. Prod.**2018**, 184, 1102–1112. [Google Scholar] [CrossRef] - Huo, T.; Cai, W.; Ren, H.; Feng, W.; Zhu, M.; Lang, N.; Gao, J. China’s building stock estimation and energy intensity analysis. J. Clean. Prod.
**2019**, 207, 801–813. [Google Scholar] [CrossRef] - Huo, T.; Ren, H.; Cai, W. Estimating urban residential building-related energy consumption and energy intensity in China based on improved building stock turnover model. Sci. Total. Environ.
**2019**, 650, 427–437. [Google Scholar] [CrossRef] - Zhang, Y.; Peng, H.; Su, B. Energy rebound effect in China’s Industry: An aggregate and disaggregate analysis. Energy Econ.
**2017**, 61, 199–208. [Google Scholar] - UNDESA. World Urbanization Prospects the 2011 Revision; United Nations: New York, NY, USA, 2012; p. 318. [Google Scholar]
- Feng, C.; Huang, J.B.; Wang, M. The driving forces and potential mitigation of energy-related CO
_{2}emissions in China’s metal industry. Resour. Policy**2018**, 59, 487–494. [Google Scholar] [CrossRef] - Wang, M.; Feng, C. Exploring the driving forces of energy-related CO
_{2}emissions in China’s construction industry by utilizing production-theoretical decomposition analysis. J. Clean. Prod.**2018**, 202, 710–719. [Google Scholar] [CrossRef] - Zhang, W.; Jiang, L.; Cui, Y.; Xu, Y.; Wang, C.; Yu, J.; Streets, D.G.; Lin, B. Effects of urbanization on airport CO2 emissions: A geographically weighted approach using nighttime light data in China. Resour. Conserv. Recycl.
**2019**, 150, 104454. [Google Scholar] [CrossRef] - Zuo, J.; Xia, B.; Barker, J.; Skitmore, M.; Skitmore, M. Green buildings for greying people: A case study of a retirement village in Australia. Facilities
**2014**, 32, 365–381. [Google Scholar] [CrossRef] - Zhang, X. Toward a regenerative sustainability paradigm for the built environment: from vision to reality. J. Clean. Prod.
**2014**, 65, 3–6. [Google Scholar] [CrossRef] [Green Version] - Huo, T.; Tang, M.; Cai, W.; Ren, H.; Liu, B.; Hu, X. Provincial total-factor energy efficiency considering floor space under construction: An empirical analysis of China’s construction industry. J. Clean. Prod.
**2020**, 244, 118749. [Google Scholar] [CrossRef] - Zhang, W.; Lu, Z.; Xu, Y.; Wang, C.; Gu, Y.; Xu, H.; Streets, D.G. Black carbon emissions from biomass and coal in rural China. Atmos. Environ.
**2018**, 176, 158–170. [Google Scholar] [CrossRef] - Zuo, J.; Zhao, Z.-Y. Green building research—Current status and future agenda: A review. Renew. Sustain. Energy Rev.
**2014**, 30, 271–281. [Google Scholar] [CrossRef] - Zhang, X.; Zhao, X.; Jiang, Z.; Shao, S. How to achieve the 2030 CO2 emission-reduction targets for China’s industrial sector: Retrospective decomposition and prospective trajectories. Glob. Environ. Chang.
**2017**, 44, 83–97. [Google Scholar] [CrossRef] - Yuan, X.; Zuo, J. Transition to low carbon energy policies in China—From the Five-Year Plan perspective. Energy Policy
**2011**, 39, 3855–3859. [Google Scholar] [CrossRef] - NBSC. China Statistical Yearbook 2016; China Statistics Press: Beijing, China, 2016. [Google Scholar]
- Hong, L.; Zhou, N.; Feng, W.; Khanna, N.; Fridley, D.; Zhao, Y.; Sandholt, K. Building stock dynamics and its impacts on materials and energy demand in China. Energy Policy
**2016**, 94, 47–55. [Google Scholar] [CrossRef] - Zhao, D.; McCoy, A.P.; Du, J.; Agee, P.; Lu, Y. Interaction effects of building technology and resident behavior on energy consumption in residential buildings. Energy Build.
**2017**, 134, 223–233. [Google Scholar] [CrossRef] - Zhao, D.; McCoy, A.P.; Du, J. An empirical study on the energy consumption in residential buildings after adopting green building standards. Procedia Eng.
**2016**, 145, 766–773. [Google Scholar] [CrossRef] [Green Version] - Vásquez, F.; Løvik, A.N.; Sandberg, N.H.; Müller, D.B. Dynamic type-cohort-time approach for the analysis of energy reductions strategies in the building stock. Energy Build.
**2016**, 111, 37–55. [Google Scholar] [CrossRef] - Zhao, W.; Leeftink, R.; Rotter, V.S. Evaluation of the economic feasibility for the recycling of construction and demolition waste in China—The case of Chongqing. Resour. Conserv. Recycl.
**2010**, 54, 377–389. [Google Scholar] [CrossRef] - Zhang, Y.-J.; Hao, J.-F.; Song, J. The CO
_{2}emission efficiency, reduction potential and spatial clustering in China’s industry: Evidence from the regional level. Appl. Energy**2016**, 174, 213–223. [Google Scholar] [CrossRef] - Yang, X.; Jiang, Y. Comparison of energy consumption between Chinese and foreign buildings. Research
**2007**, 6, 21–26. [Google Scholar] - Tso, G.K.; Guan, J.; Tso, G. A multilevel regression approach to understand effects of environment indicators and household features on residential energy consumption. Energy
**2014**, 66, 722–731. [Google Scholar] [CrossRef] - Kialashaki, A.; Reisel, J.R. Development and validation of artificial neural network models of the energy demand in the industrial sector of the United States. Energy
**2014**, 76, 749–760. [Google Scholar] [CrossRef] - Kelly, S. Do homes that are more energy efficient consume less energy? A structural equation model of the English residential sector. Energy
**2011**, 36, 5610–5620. [Google Scholar] [CrossRef] - Kavousian, A.; Rajagopal, R.; Fischer, M. Determinants of residential electricity consumption: Using smart meter data to examine the effect of climate, building characteristics, appliance stock, and occupants’ behavior. Energy
**2013**, 55, 184–194. [Google Scholar] [CrossRef] - Hu, M.; Van Der Voet, E.; Huppes, G. Dynamic material flow analysis for strategic construction and demolition waste management in Beijing. J. Ind. Ecol.
**2010**, 14, 440–456. [Google Scholar] [CrossRef] - Clune, S.; Morrissey, J.; Moore, T. Size matters: House size and thermal efficiency as policy strategies to reduce net emissions of new developments. Energy Policy
**2012**, 48, 657–667. [Google Scholar] [CrossRef] - Cai, W.; Wu, Y.; Zhong, Y.; Ren, H. China building energy consumption: Situation, challenges and corresponding measures. Energy Policy
**2009**, 37, 2054–2059. [Google Scholar] [CrossRef] - Müller, D.B. Stock dynamics for forecasting material flows—Case study for housing in The Netherlands. Ecol. Econ.
**2006**, 59, 142–156. [Google Scholar] [CrossRef] - Pauliuk, S.; Arvesen, A.; Stadler, K.; Hertwich, E.G. Industrial ecology in integrated assessment models. Nat. Clim. Chang.
**2017**, 7, 13–20. [Google Scholar] [CrossRef] - Moura, M.C.P.; Smith, S.J.; Belzer, D.B. 120 years of U.S. residential housing stock and floor space. PLoS ONE
**2015**, 10, e0134135. [Google Scholar] [CrossRef] [PubMed] - Hu, M.; Bergsdal, H.; Van Der Voet, E.; Huppes, G.; Müller, D.B. Dynamics of urban and rural housing stocks in China. Build. Res. Inf.
**2010**, 38, 301–317. [Google Scholar] [CrossRef] - Zhou, N.; Fridley, D.; Khanna, N.Z.; Ke, J.; McNeil, M.; Levine, M. China’s energy and emissions outlook to 2050: Perspectives from bottom-up energy end-use model. Energy Policy
**2013**, 53, 51–62. [Google Scholar] [CrossRef] - Yang, T.; Pan, Y.; Yang, Y.; Lin, M.; Qin, B.; Xu, P.; Huang, Z. CO
_{2}emissions in China’s building sector through 2050: A scenario analysis based on a bottom-up model. Energy**2017**, 128, 208–223. [Google Scholar] [CrossRef] - McNeil, M.A.; Letschert, V.E.; Stephane, R.C.; Ke, J. Bottom-Up Energy Analysis System—Methodology and Results; Lawrence Berkeley National Laboratory: Berkeley, CA, USA, 2012. [Google Scholar]
- Jiang, Y.; Yang, X. China BEC situation and the problems existing in the energy conservation works. China Constr.
**2006**, 2006, 12–13. [Google Scholar] - Fridley, D.; Zheng, N.; Zhou, N. Estimating Total Energy Consumption and Emissions of China’s Commercial and Office Buildings; Office of Scientific & Technical Information Technical Reports: Berkeley, CA, USA, 2008. [Google Scholar]
- NBSC. China Statistical Yearbook; China Statistics Press: Beijing, China, 2013. [Google Scholar]
- NBSC. China Statistical Yearbook; China Statistics Press: Beijing, China, 2007. [Google Scholar]
- MOHURD. China Urban.-Rural Construction Statistical Yearbook; China Planning Press: Beijing, China, 2016. [Google Scholar]
- Wu, Y.; Shen, J.; Zhang, X.; Skitmore, M.; Lu, W. Reprint of: The impact of urbanization on carbon emissions in developing countries: a Chinese study based on the U-Kaya method. J. Clean. Prod.
**2017**, 135, 589–603. [Google Scholar] [CrossRef] [Green Version] - Song, J. Migrant employment in urban China: Characteristics and determinants-a comparative study with rural left-behind people. Population
**2012**, 34, 32–42. [Google Scholar]

**Figure 3.**The changing trend of the floor space of buildings completed in different calibers throughout the whole country from 2001 to 2014.

**Figure 4.**Sketch map of the data loss level of the statistical indicators regarding the building-related floor space.

**Figure 8.**The commercial building floor space (BFS) and commercial floor space per employee (1996–2014).

**Figure 9.**The deviations between the model results and the statistical data as for urban residential floor space from 1996 to 2006 excluding 2001 and 2002.

**Figure 10.**Comparison between the model results and the statistical data as for rural commercial floor space from 2006 to 2014 excluding 2008.

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**MDPI and ACS Style**

Pan, L.; Zhu, M.; Lang, N.; Huo, T.
What Is the Amount of China’s Building Floor Space from 1996 to 2014? *Int. J. Environ. Res. Public Health* **2020**, *17*, 5967.
https://doi.org/10.3390/ijerph17165967

**AMA Style**

Pan L, Zhu M, Lang N, Huo T.
What Is the Amount of China’s Building Floor Space from 1996 to 2014? *International Journal of Environmental Research and Public Health*. 2020; 17(16):5967.
https://doi.org/10.3390/ijerph17165967

**Chicago/Turabian Style**

Pan, Linwei, Minglei Zhu, Ningning Lang, and Tengfei Huo.
2020. "What Is the Amount of China’s Building Floor Space from 1996 to 2014?" *International Journal of Environmental Research and Public Health* 17, no. 16: 5967.
https://doi.org/10.3390/ijerph17165967