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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

Author

Listed:
  • Minda Ma

    (Chongqing University)

  • Ran Yan

    (Chongqing University)

  • Weiguang Cai

    (Chongqing University
    Lawrence Berkeley National Laboratory)

Abstract

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 β 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.

Suggested Citation

  • Minda Ma & Ran Yan & Weiguang Cai, 2017. "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," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 89(2), pages 741-756, November.
    • Handle: RePEc:spr:nathaz:v:89:y:2017:i:2:d:10.1007_s11069-017-2990-4
    DOI: 10.1007/s11069-017-2990-4
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    References listed on IDEAS

    as
    1. Zhao, Xiaoli & Li, Na & Ma, Chunbo, 2012. "Residential energy consumption in urban China: A decomposition analysis," Energy Policy, Elsevier, vol. 41(C), pages 644-653.
    2. Theo Dijkstra, 2014. "Ridge regression and its degrees of freedom," Quality & Quantity: International Journal of Methodology, Springer, vol. 48(6), pages 3185-3193, November.
    3. Wang, Shaojian & Liu, Xiaoping, 2017. "China’s city-level energy-related CO2 emissions: Spatiotemporal patterns and driving forces," Applied Energy, Elsevier, vol. 200(C), pages 204-214.
    4. Wang, Qiang & Li, Rongrong, 2016. "Drivers for energy consumption: A comparative analysis of China and India," Renewable and Sustainable Energy Reviews, Elsevier, vol. 62(C), pages 954-962.
    5. Zhang, Ming & Guo, Fangyan, 2013. "Analysis of rural residential commercial energy consumption in China," Energy, Elsevier, vol. 52(C), pages 222-229.
    6. Chen, Han & Huang, Ye & Shen, Huizhong & Chen, Yilin & Ru, Muye & Chen, Yuanchen & Lin, Nan & Su, Shu & Zhuo, Shaojie & Zhong, Qirui & Wang, Xilong & Liu, Junfeng & Li, Bengang & Tao, Shu, 2016. "Modeling temporal variations in global residential energy consumption and pollutant emissions," Applied Energy, Elsevier, vol. 184(C), pages 820-829.
    7. Ang, B.W. & Su, Bin, 2016. "Carbon emission intensity in electricity production: A global analysis," Energy Policy, Elsevier, vol. 94(C), pages 56-63.
    8. York, Richard & Rosa, Eugene A. & Dietz, Thomas, 2003. "STIRPAT, IPAT and ImPACT: analytic tools for unpacking the driving forces of environmental impacts," Ecological Economics, Elsevier, vol. 46(3), pages 351-365, October.
    9. Wang, Ping & Wu, Wanshui & Zhu, Bangzhu & Wei, Yiming, 2013. "Examining the impact factors of energy-related CO2 emissions using the STIRPAT model in Guangdong Province, China," Applied Energy, Elsevier, vol. 106(C), pages 65-71.
    10. Zhang, Ming & Song, Yan & Li, Peng & Li, Huanan, 2016. "Study on affecting factors of residential energy consumption in urban and rural Jiangsu," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 330-337.
    11. Sun, Chuanwang & Ouyang, Xiaoling, 2016. "Price and expenditure elasticities of residential energy demand during urbanization: An empirical analysis based on the household-level survey data in China," Energy Policy, Elsevier, vol. 88(C), pages 56-63.
    12. Douak, Fouzi & Melgani, Farid & Benoudjit, Nabil, 2013. "Kernel ridge regression with active learning for wind speed prediction," Applied Energy, Elsevier, vol. 103(C), pages 328-340.
    13. Cai, W.G. & Wu, Y. & Zhong, Y. & Ren, H., 2009. "China building energy consumption: Situation, challenges and corresponding measures," Energy Policy, Elsevier, vol. 37(6), pages 2054-2059, June.
    Full references (including those not matched with items on IDEAS)

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