4.7 Article

A methodology to assess China's building energy savings at the national level: An IPAT-LMDI model approach

Journal

JOURNAL OF CLEANER PRODUCTION
Volume 143, Issue -, Pages 784-793

Publisher

ELSEVIER SCI LTD
DOI: 10.1016/j.jclepro.2016.12.046

Keywords

National building energy savings (NBES); National building energy consumption (NBEC); China's existing buildings; Building energy-efficiency (BEE) policy; IPAT model; LMDI decomposition

Funding

  1. Social Science and Humanity on Young Fund of the Ministry of Education P.R. China [15YJC630003]
  2. National Natural Science Foundation of P.R. China [71403033]
  3. Fundamental Research Fund for the Central Universities of P.R. China [106112015CDJXY030009]

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National building energy savings (NBES) plays an essential role in policymaking of China's building energy-efficiency (BEE) work. Numerous factors, such as technological progress and users' behavior, affect NBES while most of them are unquantifiable. One missing possibility along this direction is that there is currently no method to calculate China's NBES by summarizing all driving factors. To arrive at a solution, we proposed a concept of comparable building energy consumption per unit area and a method of China's NBES calculation based on an extended version of IPAT model (I = PAT, I = Human Impact, P = Population, A = Affluence, T = Technology) and LMDI decomposition (Logarithmic Mean Divisia Index, LMDI). Calculation revealed that China's NBES in The 10th Five Year Plan period (2001-2005), The 11th Five Year Plan period (2006-2010), and the first four years of The 12th Five Year Plan period (2011-2014) was 165, 158, and 127 million tce, respectively. Based on these calculation results, we checked NBES data then successfully proved the validity of this calculation method. Furthermore, after comparing the calculated NBES with the official planned NBES, we found that China surpassed its BEE targets. In the third stage of China's BEE work (2006-2015), the implementation of BEE policies obtained good results. (C) 2016 Elsevier Ltd. All rights reserved.

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