Abstract
In this article population, urbanization, construction area and the level of urban consumption are selected as the key factors of driving the growth of urban residential building energy consumption. At the same time, STIRPAT model is adopted to research quantitatively on the influence of these factors. Ridge regression is adopted to set up the driving factors regression model of urban residential building energy consumption. According to the results of the regression analysis, it is concluded as follows that firstly, the level of urban consumption has much more influence on the growth of urban residential building energy consumption than other factors, secondly increase of urban construction area directly drives the growth of urban residential building energy consumption, and thirdly the structure of population has much more influence on the growth of urban residential building energy consumption.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Cai WG (2011) Analyzing impact factors of building energy consumption: Modeling and empirical study. Chongqing University (In Chinese)
Ehrlich PR, Holden JP (1971) Impact of population growth. Science 171:1212–1217
Ehrlich PR, Holden JP (1972) One dimensional economy. Bulletin of Atomic Scientists 16:18–27
York R, Rosa EA, Dietz T (2003) STIRPAT, IPAT and ImPACT: Analytic tools for unpacking the driving forces of environmental impacts. Ecological Economics 46(3):351–365
Waggoner PE, Ausubel JH (2002) A framework for sustainability science: A renovated IPAT identity. In: Proceedings of the National Academy of Science 860–7885
Schulze PC (2002) I = PBAT. Ecological Economics 40(2):149–150
Mark D (2003) I = PAT or I = PBAT. Ecological Economics 42(1-2):3
Dietz T, Rosa EA (1994) Rethinking the environmental impacts of population, affluence and technology. Human Ecology Review 1:277–300
York R, Rosa EA, Dietz T (2003) STIRPAT, IPAT and IMPACT, analytic tools for unpacking driving forces of environmental impact. Ecological Economics 46(3):351–365
Liu L (2006) Policy modeling and empirical research on carbon emission reduction. University of Science and Technology of China (In Chinese)
Chen J, Peng X, Zhu Q (2009) Empirical research on the influence of family mode on carbon emission. Chinese Journal of Population (5):68–78 (In Chinese)
Wang H, Wu Z, Meng J (2006) Linear and nonlinear method of partial least squares regression. National Defense Industry Press (In Chinese)
Wang H (1996) Hazards of multiple correlations among variables on principal component analysis. Journal of Beijing University of Aeronautics and Astronautics (22):65–70 (In Chinese)
Hoerl AE, Kennard RW (1970) Ridge regression: Biased estimation for nonorthogonal problems. Technometrics 12(1):55–67
Hoerl AE, Kennard RW (1970) Ridge regression: Applications to nonorthogonal problems. Technometrics 12:69–82
Gao H (2000) Methods of independent variables collinearity in the multiple linear regression. Application of Statistics and Management (9):49–55 (In Chinese)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhu, Y., Cai, W. (2014). Applying STIRPAT Model to Identify Driving Factors of Urban Residential Building Energy Consumption: A Case Study of Chongqing in China. In: Xu, J., Fry, J., Lev, B., Hajiyev, A. (eds) Proceedings of the Seventh International Conference on Management Science and Engineering Management. Lecture Notes in Electrical Engineering, vol 242. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40081-0_111
Download citation
DOI: https://doi.org/10.1007/978-3-642-40081-0_111
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40080-3
Online ISBN: 978-3-642-40081-0
eBook Packages: Business and EconomicsBusiness and Management (R0)