DOI QR코드

DOI QR Code

System dynamic modeling and scenario simulation on Beijing industrial carbon emissions

  • Wen, Lei (Department of Economics and Management, North China Electric Power University) ;
  • Bai, Lu (Department of Economics and Management, North China Electric Power University) ;
  • Zhang, Ernv (Department of Economics and Management, North China Electric Power University)
  • 투고 : 2016.04.01
  • 심사 : 2016.06.29
  • 발행 : 2016.12.30

초록

Beijing, as a cradle of modern industry and the third largest metropolitan area in China, faces more responsibilities to adjust industrial structure and mitigate carbon emissions. The purpose of this study is aimed at predicting and comparing industrial carbon emissions of Beijing in ten scenarios under different policy focus, and then providing emission-cutting recommendations. In views of various scenarios issues, system dynamics has been applied to predict and simulate. To begin with, the model has been established following the step of causal loop diagram and stock flow diagram. This paper decomposes scenarios factors into energy structure, high energy consumption enterprises and growth rate of industrial output. The prediction and scenario simulation results shows that energy structure, carbon intensity and heavy energy consumption enterprises are key factors, and multiple factors has more significant impact on industrial carbon emissions. Hence, some recommendations about low-carbon mode of Beijing industrial carbon emission have been proposed according to simulation results.

과제정보

연구 과제 주관 기관 : Central Universities

참고문헌

  1. Walther GR, Post E, Convey P, et al. Ecological responses to recent climate change. Nature 2002;416:389-395. https://doi.org/10.1038/416389a
  2. Ford A. System dynamics and the electric power industry. Syst. Dynam. Rev. 1997;13:57-85. https://doi.org/10.1002/(SICI)1099-1727(199721)13:1<57::AID-SDR117>3.0.CO;2-B
  3. He J. Analysis of $CO_2$ emissions peak: China's objective and strategy. Chinese J. Popul. Resour. Environ. 2014;12:189-198. https://doi.org/10.1080/10042857.2014.932266
  4. Ouyang X, Lin B. An analysis of the driving forces of energy-related carbon dioxide emissions in china's industrial sector. Renew. Sust. Energ. Rev. 2015;45:838-849. https://doi.org/10.1016/j.rser.2015.02.030
  5. Ma C, Zhang GY, Zhang XC, et al. Simulation modeling for wetland utilization and protection based on system dynamic model in a coastal city, China. Procedia Environ. Sci. 2012;13: 202-213. https://doi.org/10.1016/j.proenv.2012.01.019
  6. Yin Y, Xu W, Zhou S. Linking carbon sequestration science with local sustainability: An integrated assessment approach. J. Environ. Manage. 2007;85:711-721. https://doi.org/10.1016/j.jenvman.2006.09.005
  7. Barth M, Boriboonsomsin K. Energy and emissions impacts of a freeway-based dynamic eco-driving system. Transport. Res. D-Tr. E. 2009;14:400-410. https://doi.org/10.1016/j.trd.2009.01.004
  8. Cui Q, Yang J, Dong W. Determinants of the variance of estimations on China's carbon emission: Based on meta-analysis. Energy Procedia 2014;61:1150-1162. https://doi.org/10.1016/j.egypro.2014.11.1043
  9. Fan Y, Liu LC, Wu G, et al. Changes in carbon intensity in China: Empirical findings from 1980-2003. Ecol. Econ. 2007;6:683-691.
  10. Wang X, Duan Z, Wu L, et al. Estimation of carbon dioxide emission in highway construction: A case study in southwest region of China. J. Clean. Prod. 2014;103:705-714.
  11. 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
  12. Fong WK, Matsumoto H, Lun YF. Application of system dynamics model as decision making tool in urban planning process toward stabilizing carbon dioxide emissions from cities. Build. Environ. 2009;44:1528-1537. https://doi.org/10.1016/j.buildenv.2008.07.010
  13. Richardson GP, AL Pugh. Introduction to system dynamics modeling. Portland (OR): Productivity Press; 1981.
  14. Hassan QU, Baek SS. How to do structural validity of a system dynamics type simulation model: The case of an energy policy model. Energ. Policy 2010;38:2216-2224. https://doi.org/10.1016/j.enpol.2009.12.009
  15. Homer JB, St. Clair CL. A model of HIV transmission through needle sharing. A model useful in analyzing public policies, such as a needle cleaning campaign. Interfaces 1991;21:26-29.
  16. Ford A, Lorber HW. Methodology for the analysis of the impacts of electric power production in the West. Paper read at Environmental Protection Agency Conference on Energy/Environment II; 1977.
  17. Abada I, Briat V, Massol O. Construction of a fuel demand function portraying interfuel substitution, a system dynamics approach. Energy 2013;49:240-251. https://doi.org/10.1016/j.energy.2012.10.063
  18. Akkermans H, Bogerd P, Vos B. Virtuous and vicious cycles on the road towards international supply chain management. Int. J. Oper. Prod. Man. 1999;19:565-582. https://doi.org/10.1108/01443579910260883
  19. Qudrat-Ullah H. Green power in Ontario: A dynamic model-based analysis. Energy 2014;77:859-870. https://doi.org/10.1016/j.energy.2014.09.072
  20. Ford A, Bull M. Using system dynamics for conservation policy analysis in the Pacific Northwest. Syst. Dynam. Rev. 1989;15:1-16.
  21. Luo D, Hu Z, Choi DG, et al. Life cycle energy and greenhouse gas emissions for an ethanol production process based on blue-green algae. Environ. Sci. Technol. 2010;44:8670-8677. https://doi.org/10.1021/es1007577
  22. Vafa-Arani H, Jahani S, Dashti H, et al. A system dynamics modeling for urban air pollution: A case study of Tehran, Iran. Transport. Res. D-Tr. E. 2014;31:21-36.
  23. Qudrat-Ullah H. On the validation of system dynamics type simulation models. Telecommun. Syst. 2012;51:159-166. https://doi.org/10.1007/s11235-011-9425-4
  24. Feng YY, Chen SQ, Zhang LX. System dynamics modeling for urban energy consumption and $CO_2$ emissions: A case study of Beijing, China. Ecol. Model. 2013;252:44-52. https://doi.org/10.1016/j.ecolmodel.2012.09.008
  25. Anand S, Vrat P, Dahiya RP. Application of a system dynamics approach for assessment and mitigation of $CO_2$ emissions from the cement industry. J. Environ. Manage. 2006;79:383-398. https://doi.org/10.1016/j.jenvman.2005.08.007
  26. Sterman JD. An integrated approach to the economic long wave.

피인용 문헌

  1. Study of the impact of energy consumption structure on carbon emission intensity in China from the perspective of spatial effects pp.1573-0840, 2018, https://doi.org/10.1007/s11069-018-3535-1