1. Proceedings
  2. I3M
  3. 2020
  4. SESDE
  5. 10.46354/i3m.2020.sesde.002

Co-simulation of energy transition in residential sectors of Chinese lower-tier cities

  • a Daniel van Bilsen , 
  • b Yilin Huang , 
  • c Li Fen
  • a,b Delft University of Technology, Faculty of Technology, Policy and Management, Jaffalaan 5, Delft, 2628 BX, The Netherlands
  • c  Shenzhen Institute of Building Research, 1F, Building 7, Zhongguancun National Defense Science and Technology Park, Haidian District, Beijing 100081, China
Cite as
van Bilsen D., Huang Y., Fen L. (2020). Co-simulation of energy transition in residential sectors of Chinese lower-tier cities. Proceedings of the 8th International Workshop on Simulation for Energy, Sustainable Development & Environment (SESDE 2020), pp. 7-18. DOI: https://doi.org/10.46354/i3m.2020.sesde.002

Abstract

China is undergoing large changes to tackle carbon dioxide emissions and air pollution. While the top-down governance allows for clear setting of emission reduction targets for industrial sectors and major cities, reducing emissions in residential sectors in smaller (the so-called low-tier) cities remain challenging and often unaddressed. This paper studies policy options to reduce emissions in residential sectors in low-tier Chinese cities. We conducted interviews and surveys in the city of Jingmen in the Hubei province and developed simulation models with feasible policy options and realistic consumption choice preferences. The simulation provided insights to the policies on reducing household coal consumption and ensuing emissions. Our research found that top-down restrictive policies such as coal ban and coal tax are effective in reducing emissions. They, however, restrict access to affordable energy for heating and cooking, especially within rural areas. They hence need to be combined with supportive policies such as electricity subsidy to yield long-term positive impact. 

References

  1. Alem, Y., Beyene, A. D., Köhlin, G., & Mekonnen, A. (2016). Modeling household cooking fuel choice: A panel multinomial logit approach. Energy Economics, 59, 129–137. https://doi.org/10.1016/j.eneco.2016.06.025
  2. Bildik, Y., Daalen, C. E. Van, Yü, G., Ortt, J. R., & Thissen, W. A. H. (2015). Modelling Wind Turbine Diffusion : A comparative study of California and Denmark 1980-1995. International Conference of the System Dynamics Society, 1–25
  3. CEIC. (2019). China Electricity Price – China CN: Service Price: 36 City Avg: Electricity: for Resident: 220v. Retrieved from: https://www.ceicdata.com/en/china/electricityprice [Accessed: 09/06/20]
  4. Climate Action Tracker. (2019). Country Summary: China. Retrieved from: https://climateactiontracker.org/countries/china/[Date Accessed: 18/10/2019]
  5. Delang, C. O. (2016). China's Air Pollution Problems. Routledge.
  6. Dong, K. Y., Sun, R. J., Li, H., & Jiang, H. D. (2017). A review of China’s energy consumption structure and outlook based on a long-range energy alternatives modeling tool. Petroleum Science, 14(1), 214–227. https://doi.org/10.1007/s12182-016-0136-z
  7. Energy Information Administration (EIA). (2020). China Emission Data. Retrieved from: https://www.eia.gov/international/data/country/CHN
  8. Gilley, B. (2012). Authoritarian environmentalism and China’s response to climate change. Environmental Politics, 21(2), 287–307. https://doi.org/10.1080/09644016.2012.651904
  9. Gu, Y., Wong, T. W., Law, C. K., Dong, G. H., Ho, K. F., Yang, Y., & Yim, S. H. L. (2018). Impacts of sectoral emissions in China and the implications: air quality, public health, crop production, and economic costs. Environmental Research Letters, 13(8), 084008.
  10. Hosier, R. H., & Dowd, J. (2002). Household fuel choice in Zimbabwe. Resources and Energy, 9(4), 347–361. https://doi.org/10.1016/0165-0572(87)90003-x
  11. Huang, Y., Bor, Y. J., & Peng, C. Y. (2011). The longterm forecast of Taiwan’s energy supply and demand: LEAP model application. Energy Policy, 39(11), 6790–6803. https://doi.org/10.1016/j.enpol.2010.10.023
  12. Huang, Y., & Warnier, M. (2019). Bridging the Attitude-Behaviour Gap in Household Energy Consumption. In 2019 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe) (pp. 1-5). IEEE.
  13. Hubei Provincial Bureau of Statistics. (2020). 荆门市统计年鉴 [Jingmen Statistical Yearbook Data]. Retrieved from:
    http://tjj.hubei.gov.cn/tjsj/sjkscx/tjnj/gsztj/jms/[Date Accessed: 04/06/2020]
  14. Hubei Province Provincial People’s Government. (2018). 荆门市城镇化率大幅提升 [Jingmen City’s urbanization rate has increased significantly]. Retrieved from: http://www.hubei.gov.cn/xxbs/szbs/jms/201805/t20180508_1284145.shtml [Date Accessed:22/10/2019]
  15. Hu, S., Yan, D., Guo, S., Cui, Y., & Dong, B. (2017). A survey on energy consumption and energy usage behavior of households and residential building in urban China. Energy & Buildings, 148(2017), 366–378. https://doi.org/10.1016/j.enbuild.2017.03.064
  16. Jia, H. (2018). China digs deep on environmental research. Retrieved from:
    https://www.natureindex.com/news-blog/chinadigs-deep-on-environmental-research [Date Accessed: 17/09/2019]
  17. Jiang, L., & O’Neill, B. C. (2004). The energy transition in rural China. International Journal of Global Energy Issues, 21(1–2), 2–26. https://doi.org/10.1504/IJGEI.2004.004691
  18. Latif, S. A., Omar, M. S., Bidin, Y. H., & Awang, Z. (2013). Role of Environmental Knowledge in Creating Pro-Environmental Residents. Procedia - Social and Behavioral Sciences, 105, 866–874. 
    https://doi.org/10.1016/j.sbspro.2013.11.088
  19. LEAP. (n.d.). TED: Technology And Environmental Database., Retrieved from:
    http://www.energycommunity.org/WebHelpPro/TED/TED_Intro.htm
  20. Liao, H., Chen, T., Tang, X., & Wu, J. (2019). Fuel choices for cooking in China: Analysis based on multinomial logit model. Journal of Cleaner Production, 225, 104–111. https://doi.org/10.1016/j.jclepro.2019.03.302
  21. Muller, C., & Yan, H. (2014). Household Fuel Use in Rural China. (June), 1–38. Retrieved from
    https://afse2016.sciencesconf.org/98089/document
  22. Molero-Simarro, R. (2017). China Economic Review Inequality in China revisited . The effect of functional distribution of income on urban top incomes , the urban-rural gap and the Gini index, 1978 – 2015. China Economic Review, 42, 101–117. https://doi.org/10.1016/j.chieco.2016.11.006
  23. National Development and Reform Commission. (2008). Main Functions of Departments of the NDRC – Department of Climate Change. Retrieved from:
    http://en.ndrc.gov.cn/mfod/200812/t20081218_252201.html [Accessed: 03/07/19]
  24. Ou, J., Meng, J., Zheng, J., Mi, Z., Bian, Y., Yu, X., …Guan, D. (2017). Demand-driven air pollutant emissions for a fast-developing region in China. Applied Energy, 204, 131–142. https://doi.org/10.1016/j.apenergy.2017.06.112
  25. Paço, A., & Lavrador, T. (2017). Environmental knowledge and attitudes and behaviours towards energy consumption. Journal of Environmental Management, 197, 384–392.
    https://doi.org/10.1016/j.jenvman.2017.03.100
  26. Qi, Y., Ma, L., Zhang, H., & Li, H. (2008). Translating a Global Issue Into Local Priority. The Journal of Environment & Development, 17(4), 379–400. https://doi.org/10.1177/1070496508326123
  27. Sahu, B. K. (2018). Wind energy developments and policies in China : A short review. Renewable and Sustainable Energy Reviews, 81(April 2017), 1393–1405. https://doi.org/10.1016/j.rser.2017.05.183
  28. Sandalow, D. (2018). Guide to Chinese Climate Policy 2018. Columbia University School of International
    and Public Affairs, Center on Global Energy Policy, (July), 1–139.
  29. Shin, H. C., Park, J. W., Kim, H. S., & Shin, E. S. (2005). Environmental and economic assessment of landfill gas electricity generation in Korea using LEAP model. Energy Policy, 33(10), 1261–1270.
    https://doi.org/10.1016/j.enpol.2003.12.002
  30. Stockholm Environment Institute (SEI). (2005). LEAP:Long Range Energy Alternatives Planning System, User Guide for LEAP 2005. Retrieved from: http://forums.seib.org/leap/documents/Leap2005UserGuideEnglish.pdf,2005S.
  31. Su, M., Chen, C., & Yang, Z. (2016). Urban energy structure optimization at the sector scale: Considering environmental impact based on life cycle assessment. Journal of Cleaner Production, 112, 1464–1474.
    https://doi.org/10.1016/j.jclepro.2015.01.059
  32. The People’s Government of Jingmen Municipality. (2019). About Jingmen. Retrieved from:  http://www.jingmen.gov.cn/col/col4232/index.html [Date Accessed: 22/10/2019]
  33. Wang, H., Lu, X., Deng, Y., Sun, Y., Nielsen, C. P., Liu, Y., ... & McElroy, M. B. (2019). China’s CO2 peak before 2030 implied from characteristics and growth of cities. Nature Sustainability, 2(8), 748-754.
  34. Wang, K., Wang, Y. X., Li, K., & Wei, Y. M. (2015). Energy poverty in China: An index based comprehensive evaluation. Renewable and Sustainable Energy Reviews, 47, 308–323. https://doi.org/10.1016/j.rser.2015.03.041
  35. Wang, T. J., Jin, L. S., Li, Z. K., & Lam, K. S. (2000). A modeling study on acid rain and recommended emission control strategies in China. 34.
  36. Yang, X. J., Hu, H., Tan, T., & Li, J. (2016). China’s renewable energy goals by 2050. Environmental Development, 20(October), 83–90. https://doi.org/10.1016/j.envdev.2016.10.001 
  37. Yue, T., Long, R., Liu, J., Liu, H., & Chen, H. (2019). Empirical Study on Households ’ EnergyConservation Behavior of Jiangsu Province in China : The Role of Policies and Behavior Results. https://doi.org/10.3390/ijerph16060939
  38. Zhao, S., & Alexandro, A. (2019). Current and future struggles to eliminate coal. 129(February), 511–520. https://doi.org/10.1016/j.enpol.2019.02.031