Environmental Science and Pollution Research

, Volume 26, Issue 18, pp 18814–18824 | Cite as

Impact of affluence and fossil energy on China carbon emissions using STIRPAT model

  • Yulong ZhangEmail author
  • Qingyu Zhang
  • Binbin Pan
Research Article


Using the extended STIRPAT model, this research examines the influence of various factors on China carbon emission from 1971 to 2014, including total nuclear and alternative energy, total fossil energy, GDP per capita, total population, total urban population, merchandise trade of GDP, and services value added of GDP. Ridge regression was employed to perform the study. The research results show the positivity and significance of all factors on carbon emission. The estimated elastic coefficients reveal the most important factor influencing carbon emission is GDP per capita. Total fossil energy, total urban population, and nuclear energy of total energy use are also prominent influencing factors, while other factors such as value-added services of GDP and merchandise trade of GDP have less significant impacts on carbon emission in China. These findings of the research will be of great significance for China to control its carbon emission in the future and to mitigate global warming to some extent.


CO2 emissions STIRPAT model Ridge regression China 



  1. Ahmad M, Zhao ZY, Li H (2019) Revealing stylized empirical interactions among construction sector, urbanization, energy consumption, economic growth and CO2 emissions in China. Sci Total Environ 657:1085–1098Google Scholar
  2. Antonakakis N, Chatziantoniou I, Filis G (2017) Energy consumption, CO 2, emissions, and economic growth: an ethical dilemma. Renew Sustain Energy Rev 68(Part 1):808–824Google Scholar
  3. Azizalrahman H, Hasyimi V (2019) A model for urban sector drivers of carbon emissions. Sustain Cities Soc 44:46–55Google Scholar
  4. Bai Y, Deng X, Gibson J, Zhao Z, Xu H (2019) How does urbanization affect residential CO2 emissions? An analysis on urban agglomerations of China. J Clean Prod 209:876–885Google Scholar
  5. Chang M, Zheng J, Inoue Y, Tian X, Chen Q, Gan T (2018) Comparative analysis on the socioeconomic drivers of industrial air-pollutant emissions between Japan and China: insights for the further-abatement period based on the LMDI method. J Clean Prod 189:240–250Google Scholar
  6. Chen Y, Lin S (2015) Decomposition and allocation of energy-related carbon dioxide emission allowance over provinces of China. Nat Hazards 76(3):1893–1909Google Scholar
  7. Chertow MR (2010) The IPAT equation and its variants. J Ind Ecol 4(4):13–29Google Scholar
  8. Climate Change (2007) Working group, I: the physical science basis. IPCC, Geneva, p 2007Google Scholar
  9. Climate Change (2013) The physical science basis, IPCC fifth assessment report WGI AR5.2013Google Scholar
  10. Dietz T, Rosa EA (1997) Effects of population and affluence on CO2 emissions. Proc Natl Acad Sci U S A 94:175–179Google Scholar
  11. Dietz T, Rosa EA (2002) Bridging environmental science with environmental policy: plasticity of population, affluence, and technology. Soc Sci Q 83(1):18–34Google Scholar
  12. Gill AR, Viswanathan KK, Hassan S (2018) A test of environmental Kuznets curve (EKC) for carbon emission and potential of renewable energy to reduce green house gases (GHG) in Malaysia. Environ Dev Sustain (2):1–12Google Scholar
  13. Gokmenoglu KK, Taspinar N (2018) Testing the agriculture-induced EKC hypothesis: the case of Pakistan. Environ Sci Pollut Res:1–13Google Scholar
  14. Hoerl AE, Kennard RW (1970) Ridge regression: applications to nonorthogonal problems. Technometrics 12(1):69–82Google Scholar
  15. Hoerl AE, Kannard RW, Baldwin KF (1975) Ridge regression: some simulations. Communications in Statistics 4(2):105–123Google Scholar
  16. Hoesly R, Matthews HS, Hendrickson C (2015) Energy and emissions from U.S. population shifts and implications for regional GHG mitigation planning. Environ Sci Technol 49(21):12670–12678Google Scholar
  17. Hubacek K, Feng K, Chen B (2011) Changing lifestyles towards a low carbon economy: an IPAT analysis for China. Energies 5(1):22–31Google Scholar
  18. Hughes TP, Kerry JT, Baird AH, Connolly SR, Dietzel A, Eakin CM, Heron SF, Hoey AS, Hoogenboom MO, Liu G, McWilliam MJ, Pears RJ, Pratchett MS, Skirving WJ, Stella JS, Torda G (2018) Global warming transforms coral reef assemblages. Nature 556(7702):492–496Google Scholar
  19. Khan AQ, Saleem N, Fatima ST (2018) Financial development, income inequality, and CO2 emissions in Asian countries using STIRPAT model. Environ Sci Pollut Res Int 25(7):1–12Google Scholar
  20. Li S, Wang S (2019) Examining the effects of socioeconomic development on China’s carbon productivity: a panel data analysis. Sci Total Environ 659:681–690Google Scholar
  21. Li YN, Cai M, Wu K, Wei J (2019) Decoupling analysis of carbon emission from construction land in Shanghai. J Clean Prod 210:25–34Google Scholar
  22. Liang Y, Niu D, Wang H, Li Y (2017) Factors affecting transportation sector CO2 emissions growth in China: an LMDI decomposition analysis. Sustainability 9(10):1730Google Scholar
  23. Liu D, Guo X, Xiao B (2019a) What causes growth of global greenhouse gas emissions? Evidence from 40 countries. Sci Total Environ 661:750–766Google Scholar
  24. Liu S, Fan F, Zhang J (2019b) Are small cities more environmentally friendly? An empirical study from China. Int J Environ Res Public Health 16:727Google Scholar
  25. Lu Y, Cui P, Li D (2017) Which activities contribute most to building energy consumption in China? A hybrid LMDI decomposition analysis from year 2007 to 2015. Energ Build:165Google Scholar
  26. Ma L, Chong C, Zhang X, Liu P, Li W, Li Z, Ni W (2018) LMDI decomposition of energy-related CO2 emissions based on energy and CO2 allocation Sankey diagrams: the method and an application to China. Sustainability 10(2):344Google Scholar
  27. Marquardt D, Snee R (1975) Ridge regression in practice. Am Stat 29(1):3–20Google Scholar
  28. Moutinho V, Madaleno M, Inglesi-Lotz R, Dogan E (2018) Factors affecting CO2 emissions in top countries on renewable energies: a LMDI decomposition application. Renew Sustain Energy Rev 90:605–622Google Scholar
  29. Netherlands Environmental Assessment Agency (2015) Data were from
  30. Panayotou T (1993) Empirical tests and policy analysis of environmental degradation at different stages of economic development. International Labor office, Technology and Employment Programme, Geneva. Working PapersGoogle Scholar
  31. Piłatowska M, Włodarczyk A (2018) Decoupling economic growth from carbon dioxide emissions in the EU countries. Montenegrin Journal of Economics 14:7–26Google Scholar
  32. Roy M, Basu S, Pal P (2017) Examining the driving forces in moving toward a low carbon society: an extended STIRPAT analysis for a fast growing vast economy. Clean Techn Environ Policy 19(9):2265–2276Google Scholar
  33. Sarkodie SA (2018) The invisible hand and EKC hypothesis: what are the drivers of environmental degradation and pollution in Africa? Environ Sci Pollut Res 11:1–30Google Scholar
  34. Shahbaz M, Loganathan N, Muzaffar AT, Ahmed K, Ali Jabran M (2016) How urbanization affects CO 2, emissions in Malaysia? The application of STIRPAT model. Renew Sustain Energy Rev 57:83–93Google Scholar
  35. Shahbaz M, Chaudhary AR, Ozturk I (2017) Does urbanization cause increasing energy demand in Pakistan? Empirical evidence from STIRPAT model. Energy 122:83–93Google Scholar
  36. Shi L, Sun J, Lin J, Zhao Y (2019) Factor decomposition of carbon emissions in Chinese megacities. J Environ Sci 75:209–215Google Scholar
  37. Tang X, Jin Y, Wang X, Wang J, McLellan BC (2017) Will China’s trade restructuring reduce CO 2 emissions embodied in international exports? J Clean Prod 161:1094–1103Google Scholar
  38. Ulucak R, Bilgili F (2018) A reinvestigation of EKC model by ecological footprint measurement for high, middle and low income countries. J Clean Prod 188:144–157Google Scholar
  39. Wang Q, Yang X (2019) Urbanization impact on residential energy consumption in China: the roles of income, urbanization level, and urban density. Environ Sci Pollut Res Int 26:3542–3555Google Scholar
  40. Wang P, Wu W, Zhu B, Wei Y (2013) Examining the impact factors of energy-related CO 2, emissions using the STIRPAT model in Guangdong Province, China. Appl Energy 106(11):65–71Google Scholar
  41. Wang C, Wang F, Zhang X, Yang Y, Su Y, Ye Y, Zhang H (2017) Examining the driving factors of energy related carbon emissions using the extended STIRPAT model based on IPAT identity in Xinjiang. Renew Sustain Energy Rev 67:51–61Google Scholar
  42. Wang Y, Yang G, Dong Y et al (2018) The scale, structure and influencing factors of total carbon emissions from households in 30 provinces of China—based on the extended STIRPAT model. Energies:11Google Scholar
  43. World Bank (2018) website
  44. Xiao H, Sun KJ, Bi HM, Xue JJ (2019) Changes in carbon intensity globally and in countries: attribution and decomposition analysis. Appl Energy 235:1492–1504Google Scholar
  45. Xu X, Mu M, Wang Q (2017) Recalculating CO 2 emissions from the perspective of value-added trade: an input-output analysis of China’s trade data. Energy Policy 107:158–166Google Scholar
  46. Xu SC, Miao Y-M, Gao C, Long RY, Chen H, Zhao B, Wang SX (2019) Regional differences in impacts of economic growth and urbanization on air pollutants in China based on provincial panel estimation. J Clean Prod 208:340–352Google Scholar
  47. Yang L, Lin B (2016) Carbon dioxide-emission in China’s power industry: evidence and policy implications. Renew Sustain Energy Rev 60:258–267Google Scholar
  48. Yang L, Xia H, Zhang X, Yuan S (2018) What matters for carbon emissions in regional sectors? A China study of extended STIRPAT model. J Clean Prod 180:595–602Google Scholar
  49. York R, Rosa EA, Dietz T (2015) STIRPAT, IPAT and ImPACT. Ecol Econ 46(3):351–365Google Scholar
  50. Zhang P, He J, Hong X, Zhang W, Qin C, Pang B, Li Y, Liu Y (2017) Regional-level carbon emissions modelling and scenario analysis: a STIRPAT case study in Henan Province, China. Sustainability 9(12):2342Google Scholar
  51. Zhang H, Zhu Z, Fan Y (2018) The impact of environmental regulation on the coordinated development of environment and economy in China. Nat Hazards 91(3):1–17Google Scholar
  52. Zhao S, Liao J, Yu D (2018a) Model averaging estimator in ridge regression and its large sample properties. Stat Pap (4):1–21Google Scholar
  53. Zhao Z, Bai Y, Wang G, Chen J, Yu J, Liu W (2018b) Land eco-efficiency for new-type urbanization in the Beijing-Tianjin-Hebei region. Technol Forecast Soc Chang 137:19–26Google Scholar

Copyright information

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

Authors and Affiliations

  1. 1.College of ManagementShenzhen UniversityShenzhenChina
  2. 2.College of FinanceGuizhou University of Finance and EconomicsGuiyangChina

Personalised recommendations