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An investigation and forecast on CO2 emission of China: Case studies of Beijing and Tianjin

  • Wen, Lei (The Academy of Baoding Low-Carbon Development) ;
  • Ma, Zeyang (Department of Economics and Management, North China Electric Power University) ;
  • Li, Yue (Department of Economics and Management, North China Electric Power University) ;
  • Li, Qiao (Department of Economics and Management, North China Electric Power University)
  • Received : 2017.02.22
  • Accepted : 2017.06.18
  • Published : 2017.12.31

Abstract

$CO_2$ emission is increasingly focused by public. Beijing and Tianjin are conceived to be a new economic point of growth in China. However, both of them are suffering serious environmental stress. In order to seek for the effect of socioeconomic factors on the $CO_2$ emission of this region, a novel methodology -symbolic regression- is adopted to investigate the relationship between $CO_2$ emission and influential factors of Beijing and Tianjin. Based on this method, $CO_2$ emission models of Beijing and Tianjin are built respectively. The models results manifested that Beijing and Tianjin own different $CO_2$ emission indicators. The RMSE of models in Beijing and Tianjin are 255.39 and 603.99, respectively. Further analysis on indicators and forecast trend shows that $CO_2$ emission of Beijing expresses an inverted-U shaped curve, whilst Tianjin owns a monotonically increasing trend. From analytical results, it could be argued that the diversity rooted in different development orientation and the mixture of different natural and industrial environment. This research further expands the investigation on $CO_2$ emission of Beijing and Tianjin region, and can be used for reference in the study of carbon emissions in similar regions. Based on the investigation, several policy suggestions are presented.

Keywords

Beijing and Tianjin;$CO_2$ emission;Driven factors;Genetic programming;Pareto front;Symbolic regression

References

  1. Ehrlich PR, Holdren JP. Impact of population growth. Science 1971;171:1212-1217. https://doi.org/10.1126/science.171.3977.1212
  2. Hubacek K, Feng K, Chen B. Changing lifestyles towards a low carbon economy: An IPAT analysis for China. Energies 2012;5:22-31.
  3. Waggoner PE, Ausubel JH. A framework for sustainability science: A renovated IPAT identity. P. Natl. Acad. Sci. USA. 2002;99:7860-7865. https://doi.org/10.1073/pnas.122235999
  4. York R, Rosa EA, Dietz T. STIRPAT, IPAT and ImPACT: Analytic tools for unpacking the driving forces of environmental impacts. Ecol. Econ. 2003;46:351-365. https://doi.org/10.1016/S0921-8009(03)00188-5
  5. Dietz T, Rosa EA. Effects of population and affluence on $CO_2$ emissions. P. Natl. Acad. Sci. USA. 1997;94:175-179. https://doi.org/10.1073/pnas.94.1.175
  6. Dietz T, Rosa EA. Rethinking the environmental impacts of population, affluence and technology. Hum. Ecol. Rev. 1994;1:277-300.
  7. Wang C, Chen J, Zou J. Decomposition of energy-related $CO_2$ emission in China: 1957-2000. Energy 2005;30:73-83. https://doi.org/10.1016/j.energy.2004.04.002
  8. Valipour M, Banihabib ME, Behbahani SMR. Comparison of the ARMA, ARIMA, and the autoregressive artificial neural network models in forecasting the monthly inflow of Dez dam reservoir. J. Hydrol. 2013;476:433-441. https://doi.org/10.1016/j.jhydrol.2012.11.017
  9. Valipour M. Variations of land use and irrigation for next decades under different scenarios. Braz. J. Irrigation Drainage 2016;1:262-288.
  10. Valipour M. Analysis of potential evapotranspiration using limited weather data. Appl. Water Sci. 2017;7:187-197.
  11. Valipour M. How much meteorological information is necessary to achieve reliable accuracy for rainfall estimations? Agriculture 2016;6:1-9.
  12. Valipour M, Sefidkouhi MAG. Temporal analysis of reference evapotranspiration to detect variation factors. Int. J. Global Warm. (in press).
  13. Valipour M, Sefidkouhi MAG, Raeini-Sarjaz M. Selecting the best model to estimate potential evapotranspiration with respect to climate change and magnitudes of extreme events. Agr. Water Manage. 2017;180:50-60. https://doi.org/10.1016/j.agwat.2016.08.025
  14. Azam M, Khan AQ. Testing the Environmental Kuznets Curve hypothesis: A comparative empirical study for low, lower middle, upper middle and high income countries. Renew. Sust. Energ. Rev. 2016;63:556-567. https://doi.org/10.1016/j.rser.2016.05.052
  15. Yang G, Sun T, Wang J, Li X. Modeling the nexus between carbon dioxide emissions and economic growth. Energ. Policy 2015;86:104-117. https://doi.org/10.1016/j.enpol.2015.06.031
  16. Dinda S. Environmental Kuznets Curve hypothesis: A survey. Ecol. Econ. 2004;49:431-455. https://doi.org/10.1016/j.ecolecon.2004.02.011
  17. Kaika D, Zervas E. The Environmental Kuznets Curve (EKC) theory - Part A: Concept, causes and the $CO_2$ emissions case. Energ. Policy 2013;62:1392-1402. https://doi.org/10.1016/j.enpol.2013.07.131
  18. Asumadu-Sarkodie S, Owusu PA. Energy use, carbon dioxide emissions, GDP, industrialization, financial development, and population, a causal nexus in Sri Lanka: With a subsequent prediction of energy use using neural network. Energ. Source. Part B. 2016;11:889-899. https://doi.org/10.1080/15567249.2016.1217285
  19. Asumadu-Sarkodie S, Owusu PA. Forecasting Nigeria's energy-use by 2030, an econometric approach. Energ. Source. Part B. 2016;11:990-997. https://doi.org/10.1080/15567249.2016.1217287
  20. Asumadu-Sarkodie S, Owusu PA. The impact of energy, agriculture, macroeconomic and human-induced indicators on environmental pollution: Evidence from Ghana. Environ. Sci. Pollut. Res. 2017;24:6622-6633. https://doi.org/10.1007/s11356-016-8321-6
  21. Asumadu-Sarkodie S, Owusu PA. A multivariate analysis of carbon dioxide emissions, electricity consumption, economic growth, financial development, industrialization and urbanization in Senegal. Energ. Source. Part B. 2016;12:77-84.
  22. Shahbaz M, Loganathan N, Muzaffar AT, Ahmed K, Jabran MA. How urbanization affects $CO_2$ emissions in Malaysia? The application of STIRPAT model. Renew. Sust. Energ. Rev. 2016;57:83-93. https://doi.org/10.1016/j.rser.2015.12.096
  23. Ang B, Zhang F, Choi K-H. Factorizing changes in energy and environmental indicators through decomposition. Energy 1998;23:489-495. https://doi.org/10.1016/S0360-5442(98)00016-4
  24. Asumadu-Sarkodie S, Owusu PA. Carbon dioxide emissions, GDP, energy use and population growth: A multivariate and causality analysis for Ghana, 1971-2013. Environ. Sci. Pollut. Res. 2016;23:13508-13520. https://doi.org/10.1007/s11356-016-6511-x
  25. Koza JR. Genetic programming: On the programming of computers by means of natural selection. London: The MIT Press; 1992.
  26. Burke EK, Kendall G. Search methodologies: Introductory tutorials in optimization and decision support techniques. 2nd ed. New York: Springer; 2014.
  27. Schmidt M, Lipson H. Distilling free-form natural laws from experimental data. Science 2009;324:81-85. https://doi.org/10.1126/science.1165893
  28. Chattopadhyay I, Kuchina A, Suel GM, Lipson H. Inverse Gillespie for inferring stochastic reaction mechanisms from intermittent samples. P. Natl. Acad. Sci. USA. 2013;110:12990-12995. https://doi.org/10.1073/pnas.1214559110
  29. Lau LS, Choong CK, Eng YK. Investigation of the environmental Kuznets curve for carbon emissions in Malaysia: DO foreign direct investment and trade matter? Energ. Policy 2014;68:490-497. https://doi.org/10.1016/j.enpol.2014.01.002
  30. Azlina AA, Law SH, Nik Mustapha NH. Dynamic linkages among transport energy consumption, income and $CO_2$ emission in Malaysia. Energ. Policy 2014;73:598-606. https://doi.org/10.1016/j.enpol.2014.05.046
  31. Khu ST, Liong SY, Babovic V, Madsen H, Muttil N. Genetic programming and its application in real-time runoff forecasting. J. Am. Water Resour. Assoc. 2001;37:439-451. https://doi.org/10.1111/j.1752-1688.2001.tb00980.x
  32. Bahrami P, Kazemi P, Mahdavi S, Ghobadi H. A novel approach for modeling and optimization of surfactant/polymer flooding based on Genetic Programming evolutionary algorithm. Fuel 2016;179:289-298. https://doi.org/10.1016/j.fuel.2016.03.095
  33. Palancz B, Awange J, Volgyesi L. Correction of gravimetric geoid using symbolic regression. Math Geosci. 2015;47:867-883. https://doi.org/10.1007/s11004-014-9577-3
  34. Smits G, Kotanchek M. Pareto-front exploitation in symbolic regression. Genet. Program. Theory Pract. II. 2004;8:283-299.
  35. 2050 CEACER. 2050 China energy and $CO_2$ emissions report. Beijing: Science Press; 2009.
  36. Ru G, Xiaojing C, Fengting L. 2050 Shanghai energy $CO_2$ emissions. Shanghai: Tongji University Press; 2011.
  37. Xuena W. Study on estimation method of carbon emission to energy carbon sources in China [thesis]. China: Beijing Forestry University; 2006.
  38. IPCC. 2006 IPCC guidelines for national greenhouse gas inventories. 2006.
  39. Geng Y, Tian M, Zhu Q, Zhang J, Peng C. Quantification of provincial-level carbon emissions from energy consumption in China. Renew. Sust. Energ. Rev. 2011;15:3658-3668. https://doi.org/10.1016/j.rser.2011.07.005
  40. Jin Y, Sendhoff B. Pareto-based multiobjective machine learning: An overview and case studies. IEEE Trans. Syst. Man Cybern. C. Appl. Rev. 2008;38:397-415.
  41. Jiang P, Chen J. Displacement prediction of landslide based on generalized regression neural networks with K-fold cross-validation. Neurocomputing 2016;198:40-47. https://doi.org/10.1016/j.neucom.2015.08.118
  42. Wang Z, Liu X, Zhu YB, Huang R. Prediction on Beijing's, Tianjin's and Hebei's carbon emission. Geogr. Geo-Inform. Sci. 2012;28:84-89.
  43. Zhang M, Mu H, Ning Y, Song Y. Decomposition of energy-related $CO_2$ emission over 1991-2006 in China. Ecol. Econ. 2009;68:2122-2128. https://doi.org/10.1016/j.ecolecon.2009.02.005
  44. Wang Z, Yang L. Delinking indicators on regional industry development and carbon emissions: Beijing-Tianjin-Hebei economic band case. Ecol. Indic. 2015;48:41-48. https://doi.org/10.1016/j.ecolind.2014.07.035
  45. Birdsall N. Another look at population and global warming. Policy Research Working Papers; no. WPS 1020. Population, health, and nutrition. Washington D.C.: World Bank; c1992. Available from: http://documents.worldbank.org/curated/en/985961468766195689/Another-look-at-population-and-global-warming.

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