4.6 Article

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

Journal

NATURAL HAZARDS
Volume 89, Issue 2, Pages 741-756

Publisher

SPRINGER
DOI: 10.1007/s11069-017-2990-4

Keywords

Carbon emissions in Chinese public buildings; Driving forces; STIRPAT model; Ridge regression analysis

Funding

  1. Social Science and Humanity on Young Fund of the Ministry of Education PR China [15YJC630003]
  2. Graduate Scientific Research and Innovation Foundation of Chongqing of PR China [CYB17027]
  3. Fundamental Research Fund for the Central Universities of PR China [2017CDJSK03XK01, 2017CDJSK03YJ05]

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Productive building energy efficiency work is a non-ignored booster to achieve the sustainable development in China, and evaluating the driving forces of carbon emissions in Chinese public buildings (CECPB) plays a crucial role in China building energy efficiency work. Nevertheless, China building energy efficiency work is currently challenged by the lack of effective approaches to evaluating the driving forces affecting CECPB at a quantitative level. To improve the carbon emission control strategy of Chinese public buildings, this study utilized the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model and ridge regression analysis to evaluate the driving forces affecting CECPB from 2000 to 2015. This study has three main results: (1) All of the five driving forces (i.e., population, urbanization level, floor area per capita of existing Chinese public buildings, GDP index in the Chinese tertiary industry sector, and carbon emission intensity in Chinese public buildings) have positive contributions to CECPB during the period of 2000-2015. (2) The different contributions of the aforementioned driving forces can be expressed by their different beta values in decreasing order, as follows: floor area per capita of existing Chinese public buildings (21.12%), population (20.98%), urbanization level (20.81%), carbon emission intensity in Chinese public buildings (20.20%), and GDP index in the Chinese tertiary industry sector (19.44%). (3) The goodness of fit for the final ridge regression analysis proves that the proposed evaluation method is also applicable for evaluating these driving forces at a subitem level. Furthermore, this study demonstrates the feasibility of evaluating the driving forces affecting CECPB using the STIRPAT model and ridge regression analysis and fills the research gap. The discoveries of this study can impel the development of the carbon emission control strategy of Chinese public buildings for the upcoming phase.

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