Examining the driving factors of Chinese commercial building energy consumption from 2000 to 2015: A STIRPAT model approach

Abstract

Examining the driving factors of Chinese commercial building energy consumption (CCBEC) plays an important role in Chinese building energy efficiency work. However, Chinese building energy efficiency work is currently challenged by the lack of effective approaches to examine the driving factors affecting CCBEC. To improve the constitution of the CCBEC reduction measures and strategies, the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model and ridge regression analysis were applied to examine the driving factors of CCBEC data. Results show that: (1) All of the five driving factors (i.e., population, urbanization rate, floor area per capita of existing Chinese commercial buildings, GDP index in the Chinese tertiary industry sector, and CCBEC intensity) have positive effects on CCBEC during the period of 2000-2015. (2) The importance of the five driving factors can be expressed by their different standardized beta values in decreasing order, as follows: CCBEC intensity (21.03%), floor area per capita of existing Chinese commercial buildings (20.93%), population in China (20.68%), urbanization rate in China (20.64%), and GDP index in the Chinese tertiary industry sector (19.24%). (3) The goodness of fit for the regression analysis proves that the proposed method is also applicable at the provincial or regional level. Furthermore, this study proves the feasibility of examining the driving factors affecting CCBEC using the STIRPAT model and ridge regression analysis and provides new approaches for improving the constitution of the CCBEC reduction measures and strategies.