Environmental Science and Pollution Research

, Volume 26, Issue 26, pp 27138–27147 | Cite as

Analysis of influencing factors of the carbon dioxide emissions in China’s commercial department based on the STIRPAT model and ridge regression

  • Lei WenEmail author
  • Hengyang Shao
Research Article


Commercial department assumes the vital part in energy conservation and carbon dioxide emission mitigation of China. This paper applies the time-series data covering 2001–2015 and introduces the STIRPAT method to research the factors of commercial department’s carbon dioxide emissions in China. The combination of STIRPAT method and ridge regression is first adopted to research carbon dioxide emissions of commercial department in China. Potential influencing factors of carbon dioxide emission, including economic growth, level of urbanization, aggregate population, energy intensity, energy structure and foreign direct investment, are selected to establish the extended stochastic impacts by regression on population, affluence and technology (STIRPAT) model, where ridge regression is adopted to eliminate multicollinearity. The estimation consequences show that all forces were positively related to carbon dioxide emissions in China’s commercial department except for energy structure. Energy structure is the only negative factor and aggregate population is the maximal influencing factor of carbon dioxide emissions. The economic growth, urbanization level, energy intensity and foreign direct investment all positively contribute to carbon dioxide emissions of commercial department. The findings have significant implications for policy-makers to enact emission reduction policies in commercial sector. Therefore, the paper ought to take into full consideration these different impacts of above influencing factors to abate carbon dioxide emissions of commercial sector.


China’s commercial department Carbon dioxide emission STIRPAT method Ridge regression 



  1. Babak M, Lopez NS, Lopez A, Bienvenidoet J, M. Biona JBM, F Chiu AS, Blesl M (2017) Driving forces of Iran’s CO2 emissions from energy consumption: an LMDI decomposition approach. Appl Energy 206: p. 804–814Google Scholar
  2. Brunke JC, Blesl M (2014) A plant-specific bottom-up approach for assessing the cost-effective energy conservation potential and its ability to compensate rising energy-related costs in the German iron and steel industry. Energy Policy 67(4):431–446CrossRefGoogle Scholar
  3. Chen C, Li Y, Huang G (2013) An inexact robust optimization method for supporting carbon dioxide emissions management in regional electric-power systems. Energy Econ 40(2):441–456CrossRefGoogle Scholar
  4. Choi KH, Oh W (2014) Extended Divisia index decomposition of changes in energy intensity: a case of Korean manufacturing industry. Energy Policy 65(65):275–283CrossRefGoogle Scholar
  5. Dietz T, Rosa EA (1997) Effects of population and affluence on CO2 emissions. Proceedings of the National Academy of Sciences of the United States of America 94(1): p. 175Google Scholar
  6. Feng C, Zhang H, Huang J (2017) The approach to realizing the potential of emissions reduction in China: an implication from data envelopment analysis. Renew Sust Energ Rev 71:859–872CrossRefGoogle Scholar
  7. Hoerl AE, Kennard RW (1970) Ridge regression: biased estimation for nonorthogonal problems. Technometrics 12(1):55–67CrossRefGoogle Scholar
  8. Hofmann J, Guan D, Chalvatzis K, Huo H (2016) Assessment of electrical vehicles as a successful driver for reducing CO2 emissions in China. Appl Energy 184:995–1003CrossRefGoogle Scholar
  9. Kang L, Liu Y (2015) Multi-objective optimization on a heat exchanger network retrofit with a heat pump and analysis of CO2 emissions control. Appl Energy 154(1):696–708CrossRefGoogle Scholar
  10. Kutner, Michael H (2005) Applied linear statistical models 4ed: McGraw-Hill Irwin 342Google Scholar
  11. Lee TC, Peng SK, Yeh CT, Tseng CY (2017) Bottom-up approach for downscaling CO2 emissions in Taiwan: robustness analysis and policy implications. J Environ Plan Manag 4:1–21Google Scholar
  12. Lin B, Wang A (2015) Estimating energy conservation potential in China’s commercial sector. Energy 82:147–156CrossRefGoogle Scholar
  13. Lin B, Zhao H (2016) Technological progress and energy rebound effect in China’s textile industry: evidence and policy implications. Renew Sust Energ Rev 60:173–181CrossRefGoogle Scholar
  14. Lu WT, Dai C, Fu ZH, Liang ZY, Guo HC (2018) An interval-fuzzy possibilistic programming model to optimize China energy management system with CO2 emission constraint. Energy 142:1023–1039CrossRefGoogle Scholar
  15. Miao Z, Geng Y, Sheng J (2016) Efficient allocation of CO2 emissions in China: a zero sum gains data envelopment model. J Clean Prod 112:4144–4150CrossRefGoogle Scholar
  16. Ouyang X, Lin B (2015) An analysis of the driving forces of energy-related carbon dioxide emissions in China’s industrial sector. Renew Sust Energ Rev 45:838–849CrossRefGoogle Scholar
  17. Read CB, Belsle DA (1994) Condition diagnostics, collinearity and weak data in regression. Biometrics 50(1):314CrossRefGoogle Scholar
  18. Shahbaz M, Loganathan N, Muzaffar AT, Ahmed K, Jabran MA (2016) How urbanization affects CO2 emissions in Malaysia? The application of STIRPAT model. Renew Sust Energ Rev 57:83–93CrossRefGoogle Scholar
  19. Tian S, Li K, Jiang J, Chen X, Yan F (2016) CO2 abatement from the iron and steel industry using a combined Ca–Fe chemical loop. Appl Energy 170:345–352CrossRefGoogle Scholar
  20. Wang A, Lin B (2017) Assessing CO2 emissions in China’s commercial sector: determinants and reduction strategies. J Clean Prod 164:1542–1552CrossRefGoogle Scholar
  21. Wang A, Lin B (2018) Dynamic change in energy and CO2 performance of China’s commercial sector: a regional comparative study. Energy Policy 119:113–122CrossRefGoogle Scholar
  22. Wei J, Huang K, Yang S, Li Y, Hu T, Zhang Y (2017) Driving forces analysis of energy-related carbon dioxide (CO2) emissions in Beijing: an input-output structural decomposition analysis. J Clean Prod 163:58–68CrossRefGoogle Scholar
  23. Xu B, Lin B (2015a) Carbon dioxide emissions reduction in China’s transport sector: a dynamic VAR (vector autoregression) approach. Energy 83:486–495CrossRefGoogle Scholar
  24. Xu B, Lin B (2016) Does the high-tech industry consistently reduce CO2 emissions? Results from nonparametric additive regression model. Environ Impact Assess Rev 63:44–58CrossRefGoogle Scholar
  25. Xu B, Lin B (2015c) Factors affecting carbon dioxide (CO2) emissions in China’s transport sector: a dynamic nonparametric additive regression model. J Clean Prod 101:311–322CrossRefGoogle Scholar
  26. Xu B, Lin B (2015b) How industrialization and urbanization process impacts on CO2 emissions in China: evidence from nonparametric additive regression models. Energy Econ 48:188–202CrossRefGoogle Scholar
  27. Yang Z, Shao S, Yang L, Liu J (2017) Renewable and sustainable energy reviews 72: p. 1379–1388Google Scholar
  28. Zeb R, Salar L, Awan U, Zaman K, Shahbaz M (2014) Renewable energy 71(71): p. 123–132Google Scholar

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© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Economics and ManagementNorth China Electric Power UniversityBaodingChina

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