Advances in Climate Change Research (Apr 2022)

An improved methodology for evaluating energy service demand for China's passenger transport sector

  • Jun-Ling Liu,
  • Meng-Yue Li,
  • Yuan Zeng,
  • Ming-Jian Yin,
  • Xiao-Xuan Zhang

Journal volume & issue
Vol. 13, no. 2
pp. 290 – 300

Abstract

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China's energy service demand statistics for the passenger transport sector only cover travel activities for business purpose. The incomplete data have made it very difficult to conduct long-term scenario analysis for this sector. Thus, this study develops a methodology by refining and combining vehicle activity and travel behavior methods. By taking advantage of the two methods, the extent of uncertainty in estimates can be reduced. A detailed description of China's energy service demand in the passenger transport sector for the years 2013, 2015, and 2017 are produced. Results show that there is a significant underestimation of total passenger transport turnover in governmental statistics. After reevaluation, the total passenger turnover doubled and increased by 146%, 167%, and 187%, resulting in national total passenger turnover of 6783, 8031, and 9406 billion passenger kilometers in 2013, 2015, and 2017, respectively. The majority of missing statistics are caused by not accounting for non-operational road transport, which is dominated by private vehicles. After adjustment, the road sector share in total passenger turnover grew substantially from 30% to 40% to approximately 75%, with the proportion of urban travel continuing to increase. We found that without complete data on China's passenger turnover, it may result in more than three-fold overestimation for future travel demand, leading to inaccurate projections with much higher energy consumption and CO2 emissions. It is therefore very important to have a detailed and precise calculation of energy service demand in the passenger transport sector. The estimation framework and step-by-step process can also be applied to other developing countries which are confronting similar statistical issues.

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