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.

Effect of natural resources extraction on energy consumption and carbon dioxide emission in Ghana

“…Since its advancement, the STIRPAT model has been employed by many researches including Uddin et al, (2016), Hassan (2016), Li and Lin (2015) and Shahbaz et al, (2015a) to investigate the possible factors of carbon emission for countries. Others including Wang and Han (2016), Shahbaz et al, (2015b), Inglesi-Lotz and Morales (2017), Salim and Shafiei (2014) and Ma et al, (2017) have also employed the STIRPAT model to estimate the drivers of energy consumption. The results from all these studies have not been uniform.…”

“…Their ridge regression estimation results show that among other things that the household consumption increases energy consumption; population has a small impact on energy consumption; but the distribution of urban and rural population factors (urbanization rate) have greater influence than the total population factor. Ma et al, (2017) also report in their study that population, urbanization rate, floor area per capita of existing Chinese commercial buildings, GDP index in the Chinese tertiary industry sector, and energy intensity have positive effect on Chinese commercial building energy consumption.…”

“…In their study, Yang et al (2018) among others also found that urbanization, population, energy intensity, trade openness, cooling degree days and mean temperature anomaly increased CO 2 emissions for China. Ma et al (2017) reported that population, urbanization rate, floor area per capita of existing Chinese commercial buildings, GDP index in the Chinese tertiary industry sector, and energy intensity have a positive effect on Chinese commercial building energy consumption.…”

Carbon Neutral Roadmap of Commercial Building Operations by Mid-Century: Lessons from China

“…To address the first query listed in the Introduction, namely past emissions abatement in commercial building operations, Figure 3 illustrates the past abatement of CO 2 emissions from commercial building operations during 2001-2018, which was evaluated through Equations ( 6) and (7). To express the uncertainty of the abatement results at different scales, this study considered the annual error bar in the following three values: ±31.92 MtCO 2 for total abatement, ±2.52 kgce•m −2 for abatement per floor space, and ±22.59 kgCO 2 for abatement per capita.…”

“…In the process of achieving carbon neutrality, commercial buildings are still facing rapidly increasing energy demand from the active service industry, which leads to commercial buildings being the "last mile" sector in the carbon neutral transition [6]. Specifically, carbon emissions released by commercial building operations have grown rapidly, at an annual rate of 6.83% during the past decade [7], and total emissions in 2018 reached 881 mega-tons of CO 2 (Mt CO 2 ) [8].…”