Journal of Systems Science and Complexity

, Volume 31, Issue 5, pp 1244–1272 | Cite as

The Influencing Factors of sCER Price Dynamics Under the Clean Development Mechanism: Theory and Econometric Analysis

  • Chen ZhangEmail author
  • Yaqi Wu
  • Yu Yang


In order to explore the factors and their complex mechanism affecting the price dynamics under the clean development mechanism (CDM), this article employs the secondary Certified Emission Reduction (sCER) carbon price as the study object, and analyzes its influencing factors from aspects of the international carbon-reduction policies, macroeconomic fluctuations, energy and similar carbon products prices. The innovation of this paper lies in: Introducing necessary factor (the developing countries pricing power) and the application of several international representative indicators to underline the “world” nature of CDM; utilizing different econometric models to obtain noteworthy and more robust results. The authors test the theoretical findings with multiple stationary time series from the launch of CDM to present (2008–2016). The results reveal that sCER price fluctuation shows the characteristic of asymmetry and substantial persistence. There is a strong statistically significant relationship between macroeconomic conditions, coal and oil prices, with the price of sCER. The authors discover that the pricing power of developing countries indeed has a clear but small impact on the sCER price changes, whereas the price elasticity of supply under CDM is so weak. The interaction between EU emission allowances (EUAs) and sCER presents a shift from dependency to substitution.


Clean development mechanism econometric analysis influencing factors sCER carbon price 


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The authors are grateful to the Editor as well as two anonymous referees whose insightful suggestions and comments greatly improved the quality of this paper. We also thank the National Natural Science Foundation of China under Grant [71373065] for supporting this research work. The usual disclaimer applies.


  1. [1]
    IPCC, Climate change 2013: The physical science basis, In Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change, Cambridge University Press, Cambridge, 2013.Google Scholar
  2. [2]
    Peng J, Xiao W, Wei Q, et al., Development situation of global carbon market and its enlightenment for Chinese carbon market construction, Environmental Science & Technology, 2013, 26(5): 59–62.MathSciNetGoogle Scholar
  3. [3]
    World Bank 2012, State and Trends of the Carbon Market 2012, World Bank Report, Washington, D.C.Google Scholar
  4. [4]
    Chevallier J, Detecting instability in the volatility of carbon prices, Energy Economics, 2011, 33(1): 99–110.MathSciNetCrossRefGoogle Scholar
  5. [5]
    Edenhofer O, Jakob M, Creutzig F, et al., Closing the emission price gap, Global Environmental Change, 2015, 31: 132–143.CrossRefGoogle Scholar
  6. [6]
    World Bank 2009, State and Trends of the Carbon Market 2009, World Bank Report, Washington, D.C.Google Scholar
  7. [7]
    Nordhaus W, Designing a friendly space for technological change to slow global warming, Energy Economics, 2011, 33(4): 665–673.CrossRefGoogle Scholar
  8. [8]
    Carmichael D G, Ballouz J J, Balatbat M C A, et al., Improving the attractiveness of CDM projects through allowing and incorporating options, Energy Policy, 2015, 86: 784–791.CrossRefGoogle Scholar
  9. [9]
    Xu L and Chen Y, Global climate governance and China’s strategic choice, World Economics & Politics, 2013, 1: 116–159 (in Chinese).Google Scholar
  10. [10]
    Hintermann B, Peterson S, and Rickels W, Price and market behavior in Phase II of the EU ETS: A review of the literature, Review of Environmental Economics & Policy, 2016, 10(1): 108–128.CrossRefGoogle Scholar
  11. [11]
    Chevallier J, Carbon futures and macroeconomic risk factors: A view from the EU ETS, Energy Economics, 2009, 31(4): 614–625.CrossRefGoogle Scholar
  12. [12]
    Chesney M, Gheyssens J, and Taschini L, The rise of the emission markets, environmental finance and investments, Springer, Berlin Heidelberg New York, 2013, 17–57.CrossRefGoogle Scholar
  13. [13]
    Zhang C, Ding Y, and Wang W J, An innovation of estimating value at risk of international carbon market: Conditional autoregressive value at risk models with refinements from extreme value theory, Chinese Journal of Management Science, 2015, 23(11): 12–20 (in Chinese).Google Scholar
  14. [14]
    Nazifi F, Modelling the price spread between EUA and CER carbon prices, Energy Policy, 2013, 56(5): 434–445.CrossRefGoogle Scholar
  15. [15]
    Koop G and Tole L, Forecasting the European carbon market, Journal of the Royal Statistical Society: Series A (Statistics in Society), 2013, 176(3): 723–741.MathSciNetCrossRefGoogle Scholar
  16. [16]
    Koch N, Fuss S, Grosjean G, et al., Causes of the EU ETS price drop: Recession, CDM, renewable policies or a bit of everything? — New evidence, Energy Policy, 2014, 73(13): 676–685.Google Scholar
  17. [17]
    Hintermann B, Allowance price drivers in the first phase of the EU ETS, Journal of Environmental Economics & Management, 2010, 59(1): 43–56.MathSciNetCrossRefGoogle Scholar
  18. [18]
    Aatola P, Ollikainen M, and Toppinen A, Price determination in the EU ETS market: Theory and econometric analysis with market fundamentals, Energy Economics, 2013, 36(3): 380–395.CrossRefGoogle Scholar
  19. [19]
    Koch N, Dynamic linkages among carbon, energy and financial markets: A smooth transition approach, Applied Economics, 2014, 46(46): 715–729.Google Scholar
  20. [20]
    Zhang Y J and Wei Y M, Interpreting the complex impact of fossil fuel markets on the EU ETS futures markets: An empirical evidence, Management Review, 2010, 22(6): 34–41 (in Chinese).Google Scholar
  21. [21]
    Hammoudeh S, Nguyen D K, and Sousa R M, What explain the short-term dynamics of the prices of CO2, emissions? Energy Economics, 2014, 46(C): 122–135.Google Scholar
  22. [22]
    Lutz B J, Pigorsch U, and Rotfuβ W, Nonlinearity in cap-and-trade systems: The EUA price and its fundamentals, Energy Economics, 2013, 40(2): 222–232.CrossRefGoogle Scholar
  23. [23]
    Creti A, Jouvet P A, and Mignon V, Carbon price drivers: Phase I versus phase II equilibrium? Energy Economics, 2012, 34(1): 327–334.CrossRefGoogle Scholar
  24. [24]
    Mansanet-Bataller M, Chevallier J, Hervé-Mignucci M, et al., EUA and sCER phase II price drivers: unveiling the reasons for the existence of the EUA-sCER spread, Energy Policy, 2011, 39(3): 1056–1069.CrossRefGoogle Scholar
  25. [25]
    Chevallier J, EUAs and CERs: Vector autoregression, impulse response function and cointegration analysis, Economics Bulletin, 2010, 30(1): 558–576.Google Scholar
  26. [26]
    Zou Y and Wei W, The study on the impact factors of certified carbon emissions (CERs) spot markets, Journal of Financial Research, 2013, (10): 142–153 (in Chinese).Google Scholar
  27. [27]
    Huang S S and Zhang J, Study on influencing factors of China’s carbon emission rights price, Advanced Materials Research, 2014, 962–965: 1468–1471.Google Scholar
  28. [28]
    Zhu B Z, Ma S, Chevallier J, et al., Modelling the dynamics of European carbon futures price: A Zipf analysis, Economic Modelling, 2014, 38(38): 372–380.CrossRefGoogle Scholar
  29. [29]
    Tang B J, Gong P Q, and Shen C, Factors of carbon price volatility in a comparative analysis of the EUA and sCER, Annals of Operations Research, 2017, 255(1–2): 157–168.MathSciNetCrossRefzbMATHGoogle Scholar
  30. [30]
    Zhang C, Yang Y, and Zhang T, The integrated measurement about carbon finance market risk of commercial banks based on copula model, Chinese Journal of Management Science, 2015, 23(4): 61–69 (in Chinese).Google Scholar
  31. [31]
    Benz E and Truck S, Modeling the price dynamics of CO2 emission allowances, Energy Economics, 2008, 31(1): 4–15.CrossRefGoogle Scholar
  32. [32]
    Bollerslev T, Generalized autoregressive conditional heteroscedasticity, Journal of Econometrics, 1986, 31(3): 307–327.MathSciNetCrossRefzbMATHGoogle Scholar
  33. [33]
    Nelson D B, Conditional heteroskedasticity in asset returns: A new approach, Econometrica, 1991, 59(2): 347–70.MathSciNetCrossRefzbMATHGoogle Scholar
  34. [34]
    Ding Z, Granger C W J, and Engle R F, A long memory property of stock market returns and a new model, Journal of Empirical Finance, 1993, 1: 83–106.CrossRefGoogle Scholar
  35. [35]
    Zakoian J M, Threshold heteroskedastic models, Journal of Economic Dynamics & Control, 1994, 18(5): 931–955.CrossRefzbMATHGoogle Scholar
  36. [36]
    Jaraite J, Convery F, and Di Maria C, Transaction costs for firms in the EU ETS: Lessons from Ireland, Climate Policy, 2010, 10(2): 190–215.CrossRefGoogle Scholar
  37. [37]
    Xu B, Zhou S, and Hao L, Approach and practices of district energy planning to achieve low carbon outcomes in China, Energy Policy, 2015, 83: 109–122.CrossRefGoogle Scholar
  38. [38]
    Gao X, The impacts on UN climate change frameworks from other multilateral mechanisms, World Economics & Politics, 2012, (4): 59–71 (in Chinese).Google Scholar
  39. [39]
    IPCC, Climate change 2014: Mitigation of climate change, Cambridge University Press, Cambridge, on line at:

Copyright information

© Institute of Systems Science, Academy of Mathematics and Systems Science, CAS and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of ManagementHefei University of TechnologyHefeiChina
  2. 2.Key Laboratory of Process Optimization & Intelligent Decision Making of Ministry of EducationHefeiChina

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