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Forecasting the Carbon Price in China Pilot Emission Trading Scheme: A Structural Time Series Approach

  • Zhao Mengdi
  • Soo Keong YongEmail author
Chapter

Abstract

To curb carbon emission, the Chinese pilot carbon emission trading markets were implemented in 2013 to act as a testbed for the official establishment of national carbon market in 2017. As the potential largest carbon trading market in the world, finding the key factors that drive the carbon permit prices and forecasting its future prices are important for both investors and government. This study explores the prospect of forecasting the carbon permit price in three pilot markets (Beijing, Shanghai, and Shenzhen) using the structural time series modeling (STSM) approach. The result shows that this modeling approach potentially outperforms conventional time series model in forecasting capability. Statistically, the prices of energy sources are found to be uncorrelated with the permit prices in the Chinese pilot market, in contrary to the results from other studies on the European Union carbon market.

References

  1. Aatola, P., Ollikainen, M., and Toppinen, A. (2013), “Price determination in the EU ETS market: Theory and econometric analysis with market fundamentals”, Energy Economics: 36, 380–395.CrossRefGoogle Scholar
  2. Aichele, R., and Felbermayr, G. (2013), “The Effect of the Kyoto Protocol on Carbon Emissions”, Journal of Policy Analysis & Management: 32, 731–757.CrossRefGoogle Scholar
  3. Anderson, B., and Di Maria, C. (2011), “Abatement and allocation in the pilot phase of the EU ETS”, Environmental and Resource Economics: 48, 83–103.CrossRefGoogle Scholar
  4. Bo, K., and Freeman, C. (2013), “Making Sense of Carbon Market Development in China”, Carbon & Climate Law Review: 7, 194–212.CrossRefGoogle Scholar
  5. Boersen, A., and Scholtens, B. (2014), “The relationship between European electricity markets and emission allowance futures prices in phase II of the EU (European Union) emission trading scheme”, Energy; 74, 585–594.CrossRefGoogle Scholar
  6. BP. (2013), BP Statistical Review of World Energy 2013, BP.Google Scholar
  7. Broadstock, D. C., and Collins, A. (2010), “Measuring unobserved prices using the structural time-series model: The case of cycling”, Transportation Research Part A: 44, 195–200.Google Scholar
  8. Calel, R., and Dechezlepretre, A. (2013), “Environmental Policy and Directed Technological Change: Evidence from the European carbon market”, Review of Economics and Statistics: 98, 173–191CrossRefGoogle Scholar
  9. Carraro, C., and Alice, F. (2009), “The Economic and Financial Determinants of Carbon Prices”, Finance a Uver: Czech Journal of Economics & Finance: 59, 396–409.Google Scholar
  10. Chevallier, J. (2011), “A model of carbon price interactions with macroeconomic and energy dynamics”, Energy Economics, 33, 1295–1312.CrossRefGoogle Scholar
  11. Christiansen, A. C., Arvanitakis, A., Tangen, K., and Hasselknippe, H. (2005), “Price determinants in the EU emissions trading scheme”. Climate Policy, 5, 15–30.CrossRefGoogle Scholar
  12. Constantini, V., and Mazzanti. M. (2012), “On the green and innovative side of trade competitiveness? The impact of environmental policies and innovation on EU exports”, Research Policy: 41, 132–53.CrossRefGoogle Scholar
  13. Cui, L. B., Fan, Y., Zhu, L., and Bi, Q. H. (2014), “How will the emissions trading scheme save cost for achieving China’s 2020 carbon intensity reduction target?” Applied Energy, 1043–1052.CrossRefGoogle Scholar
  14. Declercq, B., Delarue, E., and D’haeseleer, W. (2011), “Impact of the economic recession on the European power sector’s CO2 emissions”. Energy Policy, 39, 1677–1686. Available at: doi:10.1016/j.enpol.2010.12.043CrossRefGoogle Scholar
  15. Dilaver, Z. (2010), “OECD-Europe natural gas demand: An outlook to 2020. In: SEEC (Surrey Energy Economics Centre)”, 8th BIEE Academic Conference. Oxford, U.K, 22–23 September 2010.Google Scholar
  16. Frunza, M.-C., Guégan, D., and Lassoudiere, A. (2010), “Forecasting Strategies for Carbon Allowances Prices: From Classic Arbitrage Pricing Theory to Switching Regimes”. International Review of Applied Financial Issues & Economics, 2, 576–596.Google Scholar
  17. Hamilton, J. D. (1994), Time Series Analysis. Princeton: Princeton University Press.Google Scholar
  18. Han, G., Olsson, M., Hallding, K., and Lunsford, D. (2012), “China’s Carbon Emission Trading: An Overview of Current Development”. FORES (Forum for Reforms, Entrepreneurship and Sustainability).Google Scholar
  19. Harvey, A. C. (1989), Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge: Cambridge University Press.Google Scholar
  20. Harvey, A. C., and Shephard, N. (1993), “Structural time series models”, in Handbook of Statistics (Eds) G. S. Maddala, C. R. Rao, and H. D. Vinod, Elsevier Science Publishers B V, Amsterdam, 261–302.Google Scholar
  21. Hintermann, B. (2010), “Allowance price drivers in the first phase of the EU ETS”. Journal of Environmental Economics and Management, 59, 43–56.CrossRefGoogle Scholar
  22. Hübler, M., Voigt, S., Löschel, A. (2014), “Designing an emissions trading scheme for China—an up-to-date climate policy assessment”. Energy Policy, 75, 57–72.CrossRefGoogle Scholar
  23. Jackman, M., and Greenidge, K. (2010), “Modelling and forecasting tourist flows to Barbados using structural time series models”. Tourism & Hospitality Research, 10, 1–13.CrossRefGoogle Scholar
  24. Jiang, J. J., Ye, B., and Ma, X. M. (2014), The construction of Shenzhen’s carbon emission trading scheme. Energy Policy, 75, 17–21.CrossRefGoogle Scholar
  25. Jotzo, F. and Löschel, A. (2014), “Emissions trading in China: Emerging experiences and international lessons”. Energy Policy, 75, 3–8.CrossRefGoogle Scholar
  26. Kim, H. S., and Koo, W. W. (2010), “Factors affecting the carbon allowance market in the US”. Energy Policy, 38, 1879–1884.CrossRefGoogle Scholar
  27. Koop, G., and Tole, L. (2013), “Forecasting the European carbon market”. Journal of the Royal Statistical Society: Series A (Statistics in Society), 176, 723–741.CrossRefGoogle Scholar
  28. Kourentzes, N., Petropoulos, F., and Trapero, J. R. (2014), “Improving forecasting by estimating time series structural components across multiple frequencies”. International Journal of Forecasting, 30, 291–302.CrossRefGoogle Scholar
  29. Lawson, A. R., Ghosh, B., and Broderick, B. (2011), “Prediction of traffic-related nitrogen oxides concentrations using Structural Time-Series models”. Atmospheric Environment, 45, 4719–4727.CrossRefGoogle Scholar
  30. Li, J., Wang, X., Zhang, Y., Kou, Q., Cai, S., (2014), “The economic impact of carbon pricing with regulated electricity prices in China—an application of a Computable General Equilibrium approach”. Energy Policy, 75, 46–56.CrossRefGoogle Scholar
  31. Liu, L. W., Zong, H. J., Zhao, E. D., Chen, C. X., and Wang, J. Z. (2014), “Can China realize its carbon emission reduction goal in 2020: From the perspective of thermal power development”. Applied Energy, 124, 199–212.CrossRefGoogle Scholar
  32. Liu, Z., Guan, D., Crawford-Brown, D., Zhang, Q., He, K., and Liu, J. (2013), “Energy policy: A low-carbon road map for China”. Nature, 500, 143–145.CrossRefGoogle Scholar
  33. Mansanet-Bataller, M., Pardo, A., and Valor, E. (2007), “CO2 Prices, Energy and Weather”. Energy Journal, 28, 73–92.CrossRefGoogle Scholar
  34. Martin, R., Muuls, M., and Wagner, U. (2011), Climate change, investment and carbon markets and prices-evidence from manager interviews. Climate Strategies, Carbon Pricing for Low-Carbon Investment Project. Available at: http://climatestrategies.org/publication/climate-change-investment-and-carbon-markets-and-prices-evidence-from-manager-interviews/
  35. Martin, R., Muuls, M., and Wagner, U. (2016), “The Impact of the European Union Emissions Trading Scheme on Regulated Firms: What Is the Evidence after Ten Years?”, Review of Environmental Economics and Policy, 10, 129–148.CrossRefGoogle Scholar
  36. Martin, Ralf, de Preux, Laure B. and Wagner, Ulrich J. (2014), “Industry compensation under relocation risk: A firm-level analysis of the EU Emissions Trading Scheme”. American Economic Review, 104, 2482–508.CrossRefGoogle Scholar
  37. Maydybura, A., and Andrew, B. (2011), “A Study of the Determinants of Emissions Unit Allowance Price in the European Union Emissions Trading Scheme”. Australasian Accounting Business & Finance Journal, 5, 123–142.Google Scholar
  38. Michieka, N. M., Fletcher, J., and Burnett, W. (2013), “An empirical analysis of the role of China’s exports on CO2 emissions”. Applied Energy, 104, 258–267CrossRefGoogle Scholar
  39. Myklebust, J., Tomasgard, A., and Westgaard, S. (2010), “Forecasting gas component prices with multivariate structural time series models”. OPEC Energy Review, 34, 82–106.CrossRefGoogle Scholar
  40. Newell, R. G., Pizer, W. A., and Raimi, D. (2013), “Carbon Markets 15 Years after Kyoto: Lessons Learned”, New Challenges. Journal of Economic Perspectives, 27, 123–146.CrossRefGoogle Scholar
  41. Peters, G. P., Weber, C. L., Guan, D., and Hubacek, K. (2007), “China’s growing CO2 emissions–a race between increasing consumption and efficiency gains”. Environmental Science & Technology. 41, 1895–1901.CrossRefGoogle Scholar
  42. Qi, S., Wang, B., Zhang, J. (2014), “Policy design of the Hubei ETS Pilot in China”. Energy Policy, 75, 31–38.CrossRefGoogle Scholar
  43. Rudolph, S., and Schneider, F. (2013), “Political barriers of implementing carbon markets in Japan: A Public Choice analysis and the empirical evidence before and after the Fukushima nuclear disaster”. Environmental Economics & Policy Studies, 15, 211–235.CrossRefGoogle Scholar
  44. SEI. (2012), China’s Carbon Emission Trading: An Experiment to Watch Closely’. Available at: http://www.sei-international.org/mediamanager/documents/Publications/china-cluster/SEI-PB-2012-China-carbon-markets.pdf
  45. Schmalensee, R., and Stavins, R. (2017), “Lessons Learned from Three Decades of Experience with Cap and Trade”, Review of Environmental Economics and Policy, 11, 59–79.CrossRefGoogle Scholar
  46. Singh, D. P., Thakur A. K. and Ram D. S. (2014), “Application of Structural Time Series model for forecasting Gram production in India”. American International Journal of Research in Science, Technology, Engineering and Mathematics, 60–62.Google Scholar
  47. Teng, F., Wang, X., Zhiqiang, L. V. (2014), “Introducing the emissions trading system to China’s electricity sector: challenges and opportunities”. Energy Policy, 75, 39–45.CrossRefGoogle Scholar
  48. Wang, P., Dai, H. C., Ren, S. Y., Zhao, D. Q., and Masui, T. (2015), “Achieving Copenhagen target through carbon emission trading: Economic impacts assessment in Guangdong Province of China”. Energy, 212.CrossRefGoogle Scholar
  49. Wang, R., Liu, W., Xiao, L., Liu, J., and Kao, W. (2011), “Viewpoint: Path towards achieving of China’s 2020 carbon emission reduction target—A discussion of low-carbon energy policies at province level”. Energy Policy, 39, 2740–2747.CrossRefGoogle Scholar
  50. Wu, L., Quian, H., Li, J., (2014), “Advancing the experiment to reality: perspectives on Shanghai pilot carbon emissions trading scheme”. Energy Policy, 75, 22–30.CrossRefGoogle Scholar
  51. Xia, Y. A. N., Fan, Y., and Wu, J. I. E. (2013), “Analysis of low-carbon production chains towards China’s emission reduction targets for 2020”. Singapore Economic Review, 58, 1–18.CrossRefGoogle Scholar
  52. Xu, S. C., He, Z. X., and Long, R. Y. (2014), “Factors that influence carbon emissions due to energy consumption in China: Decomposition analysis using LMDI”. Applied Energy, 127, 182–193.CrossRefGoogle Scholar
  53. Zhang, Y. J., Wang, A. D. and Da, Y. B. (2014), “Regional allocation of carbon emission quotas in China: Evidence from the Shapley value method”. Energy Policy, 74, 454–464.CrossRefGoogle Scholar
  54. Zhao, J., Yang, Y., and Suo, C. (2014), “Beijing energy consumption carbon emission characteristics and cause analysis”. Journal of Chemical & Pharmaceutical Research, 6, 473–477.Google Scholar
  55. Zhu, B., and Wei, Y. (2013), “Carbon price forecasting with a novel hybrid ARIMA and least squares support vector machines methodology”. Omega, 41, 517–524.CrossRefGoogle Scholar

Copyright information

© The Author(s) 2018

Authors and Affiliations

  1. 1.International Business School SuzhouXi’an Jiaotong-Liverpool UniversitySuzhouChina

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