Natural Hazards

, Volume 92, Issue 1, pp 429–441 | Cite as

Energy savings evaluation in public building sector during the 10th–12th FYP periods of China: an extended LMDI model approach

  • Minda Ma
  • Ran Yan
  • Weiguang Cai
Original Paper


Energy savings can be treated as an indicator to reveal the effectiveness of energy efficiency task (EET) in the building sector, especially in the public buildings. However, evaluating the values of energy savings in public buildings (ESPB) was challenged by the missing data sources and inadequate tools in China. To overcome these problems, this study applied an extended Logarithmic Mean Divisia Index model to examine the contributions of different impact factors affecting the public building energy consumption (PBEC) and further evaluated the ESPB values during the 10th–12th Five-Year Plan (FYP) periods in China. Results included three aspects: (1) Absolute values of the contribution of the adjusted PBEC intensity to PBEC denoted the ESPB values in China. (2) Total values of ESPB were 99.9 Mtce during the 10th–12th FYP periods of China. Concretely, the ESPB values during the three FYP periods were as follows: 71.091 Mtce (the 12th FYP period), 19.075 Mtce (the 11th FYP period), and 9.734 Mtce (the 10th FYP period). (3) Effective EET of public buildings was a strong support for the rapidly growing ESPB during the three FYP periods. Furthermore, this study suggested that China should issue the official data on energy consumption in the building sector as quickly as possible, and this action would deeply help the government design targeted plans and policies for the future EET in the building sector.


Energy savings Public buildings Energy data Extended LMDI model FYP period 

List of symbols


Public building energy consumption


Public building energy consumption (PBEC) in China


Energy savings in public buildings


Population size in China


Urbanization level in China


Floor space per capita of public buildings in China


PBEC intensity in China

\(e_{{{\text{ad}} .}}\)

Adjusted PBEC intensity in China


Service level of public buildings in China


Per capita level of PBEC in China


Conversion index between \(L_{\text{s}}\) and \(L_{\text{P}}\)


Logarithmic Mean Divisia Index

\(\Delta E_{P}\)

Contribution of \(P\) to \(E\)

\(\Delta E_{U}\)

Contribution of \(U\) to \(E\)

\(\Delta E_{f}\)

Contribution of \(f\) to \(E\)

\(\Delta E_{{L_{\text{s}} }}\)

Contribution of \(L_{\text{s}}\) to \(E\)

\(\Delta E_{{{\text{e}}_{{{\text{ad}} .}} }}\)

Contribution of \(e_{{{\text{ad}} .}}\) to \(E\)



This study was supported by the Fundamental Research Funds for the Central Universities of PR China (106112017CDJXSYY0001-KJYF201706 and 2017CDJSK03YJ05), the Graduate Research and Innovation Foundation of Chongqing, China (CYB17027), and the Fundamental Research Funds for the Central Universities of PR China (2017CDJSK03XK01).


  1. Ang BW (2005) The LMDI approach to decomposition analysis: a practical guide. Energy Policy 33(7):867–871CrossRefGoogle Scholar
  2. Ang B (2015) LMDI decomposition approach: a guide for implementation. Energy Policy 86:233–238CrossRefGoogle Scholar
  3. Berardi U (2017) A cross-country comparison of the building energy consumptions and their trends. Resour Conserv Recycl 123:230–241CrossRefGoogle Scholar
  4. Cai W, Liu F, Zhou X, Xie J (2016) Fine energy consumption allowance of workpieces in the mechanical manufacturing industry. Energy 114:623–633CrossRefGoogle Scholar
  5. Cai W, Liu F, Zhang H, Liu P, Tuo J (2017a) Development of dynamic energy benchmark for mass production in machining systems for energy management and energy-efficiency improvement. Appl Energy 202:715–725CrossRefGoogle Scholar
  6. Cai W, Liu F, Xie J, Liu P, Tuo J (2017b) A tool for assessing the energy demand and efficiency of machining systems: energy benchmarking. Energy 138:332–347CrossRefGoogle Scholar
  7. Cai W, Liu F, Xie J, Zhou X (2017c) An energy management approach for the mechanical manufacturing industry through developing a multi-objective energy benchmark. Energy Convers Manag 132:361–371CrossRefGoogle Scholar
  8. Cai W, Liu F, Dinolov O, Xie J, Liu P, Tuo J (2018) Energy benchmarking rules in machining systems. Energy 142:258–263CrossRefGoogle Scholar
  9. CABEE (2016) Chinese building energy consumption report. In: Cai WG (ed) Beijing, PR ChinaGoogle Scholar
  10. Choi K-H, Oh W (2014) Extended Divisia index decomposition of changes in energy intensity: a case of Korean manufacturing industry. Energy Policy 65:275–283CrossRefGoogle Scholar
  11. Ehrlich PR, Holdren JP (1971) Impact of population growth. Science 171(3977):1212–1217CrossRefGoogle Scholar
  12. Fujii M, Fujita T, Dong L, Lu C, Geng Y, Behera SK et al (2016) Possibility of developing low-carbon industries through urban symbiosis in Asian cities. J Clean Prod 114:376–386CrossRefGoogle Scholar
  13. Kong X, Lu S, Wu Y (2012) A review of building energy efficiency in China during “Eleventh Five-Year Plan” period. Energy Policy 41(Supplement C):624–635CrossRefGoogle Scholar
  14. Liang H, Tanikawa H, Matsuno Y, Dong L (2014) Modeling in-use steel stock in China’s buildings and civil engineering infrastructure using time-series of DMSP/OLS nighttime lights. Remote Sens 6(6):4780–4800CrossRefGoogle Scholar
  15. Liang L, Hu X, Tivendale L, Liu C (2017) The log mean divisia index based carbon productivity in the Australian construction industry. Constr Econ Build 17(3):68–84CrossRefGoogle Scholar
  16. Lin B, Liu H (2015) CO 2 emissions of China’s commercial and residential buildings: evidence and reduction policy. Build Environ 92:418–431CrossRefGoogle Scholar
  17. Liu Z, Xu W, Zhai X, Qian C, Chen X (2017a) Feasibility and performance study of the hybrid ground-source heat pump system for one office building in Chinese heating dominated areas. Renew Energy 101:1131–1140CrossRefGoogle Scholar
  18. Liu Z, Li H, Liu K, Yu H, Cheng K (2017b) Design of high-performance water-in-glass evacuated tube solar water heaters by a high-throughput screening based on machine learning: a combined modeling and experimental study. Sol Energy 142:61–67CrossRefGoogle Scholar
  19. Lynn KP, Nina K, Nan Z, David F, Ali H, Hongyou L, et al. (2017) Reinventing fire: China—the role of energy efficiency in China’s roadmap to 2050. In: Conference reinventing fire: China—the role of energy efficiency in China’s roadmap to 2050, Presqu’ile Giens, Hyeres, FranceGoogle Scholar
  20. Ma M, Yan R, Cai W (2017a) An extended STIRPAT model-based methodology for evaluating the driving forces affecting carbon emissions in existing public building sector: evidence from China in 2000–2015. Nat Hazards 89(2):741–756CrossRefGoogle Scholar
  21. Ma M, Yan R, Du Y, Ma X, Cai W, Xu P (2017b) A methodology to assess China’s building energy savings at the national level: an IPAT-LMDI model approach. J Clean Prod 143:784–793CrossRefGoogle Scholar
  22. Ma M, Yan R, Cai W (2017c) A STIRPAT model-based methodology for calculating energy savings in China’s existing civil buildings from 2001 to 2015. Nat Hazards 87(3):1765–1781CrossRefGoogle Scholar
  23. McNeil MA, Feng W, du Can SdlR, Khanna NZ, Ke J, Zhou N (2016) Energy efficiency outlook in China’s urban buildings sector through 2030. Energy Policy 97:532–539CrossRefGoogle Scholar
  24. Mi Z, Meng J, Guan D, Shan Y, Song M, Wei Y-M et al (2017) Chinese CO 2 emission flows have reversed since the global financial crisis. Nat Commun 8(1):1712CrossRefGoogle Scholar
  25. MOHURD_of_PRC (2012) “12th Five-year” Building energy-saving special PlanGoogle Scholar
  26. MOHURD_of_PRC (2017) “13th Five-year” building energy-saving and green building development planGoogle Scholar
  27. Shao S, Yang L, Gan C, Cao J, Geng Y, Guan D (2016) Using an extended LMDI model to explore techno-economic drivers of energy-related industrial CO 2 emission changes: a case study for Shanghai (China). Renew Sustain Energy Rev 55:516–536CrossRefGoogle Scholar
  28. Shuai C, Chen X, Wu Y, Tan Y, Zhang Y, Shen L (2018) Identifying the key impact factors of carbon emission in China: results from a largely expanded pool of potential impact factors. J Clean Prod 175:612–623CrossRefGoogle Scholar
  29. Wang L, Long R, Chen H (2017a) Study of urban energy performance assessment and its influencing factors based on improved stochastic frontier analysis: a case study of provincial capitals in China. Sustainability 9(7):1110CrossRefGoogle Scholar
  30. Wang Y, Liu H, Mao G, Zuo J, Ma J (2017b) Inter-regional and sectoral linkage analysis of air pollution in Beijing–Tianjin–Hebei (Jing-Jin-Ji) urban agglomeration of China. J Clean Prod 165:1436–1444CrossRefGoogle Scholar
  31. Wei W, He L-Y (2017) China building energy consumption: definitions and measures from an operational perspective. Energies 10(5):582CrossRefGoogle Scholar
  32. Yan R, Ma M, Pan T (2017) Estimating energy savings in Chinese residential buildings from 2001 to 2015: a decomposition analysis. J Eng Sci Technol Rev 10(1):107–113Google Scholar
  33. Ye H, Ren Q, Hu X, Lin T, Shi L, Zhang G et al (2018) Modeling energy-related CO2 emissions from office buildings using general regression neural network. Resour Conserv Recycl 129:168–174CrossRefGoogle Scholar

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© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Construction Management and Real EstateChongqing UniversityChongqingPeople’s Republic of China
  2. 2.Energy Analysis and Environmental Impacts Division, Energy Technologies AreaLawrence Berkeley National LaboratoryBerkeleyUSA
  3. 3.Special Committee of Building Energy Consumption StatisticsChina Association of Building Energy EfficiencyBeijingPeople’s Republic of China

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