Will China peak its energy-related carbon emissions by 2030? Lessons from 30 Chinese provinces

By Kai Fang, Yiqi Tang, Qifeng Zhang, Junnian Song, Qi Wen, Huaping Sun, Chenyang Ji, Anqi Xu. 2019

271 citations recorded by the source corpus.

Abstract China has pledged in its Nationally Determined Contributions (NDCs) that its carbon emissions will peak no later than 2030. However, the varying carbon emissions trajectories of individual provinces make it difficult to determine whether China can fulfill this ambitious goal on time. To bring transparency and clarity to this core issue, this paper explores for the first time whether and how all the 30 Chinese provinces will peak their energy-related emissions in future by developing a set of extended STIRPAT models that investigate the driving forces behind each province and integrating the estimates into scenario analysis for peaking simulation. We observe that, despite continued economic growth, 26 provinces are highly likely to reach peak emissions under at least one scenario, whereas the remaining are not. We find it interesting that the earliest peaking scenarios may not be the best option for those provinces whose cumulative carbon emissions are estimated to be considerably large. To ensure a viable reaching of China’s peak emissions, much more attention should be paid to seeking for the most appropriate emissions trajectory to peak at the subnational level with relatively low cumulative value. Provincial results in aggregate show that China may peak emissions in the period 2028–2040, with cumulative emissions ranging from 403,607 Mt CO2 (corresponding to peaking in 2030) to 456,191 Mt CO2 (corresponding to peaking in 2038), while 2030 would be the optimal year for the country to peak because of the lowest cumulative carbon emissions. Our findings only provide a deeper understanding of China’s potential peaking paths by province, but also assist policy makers in better prediction and evaluation of possible emissions trajectory for other nations and the whole world from a bottom-up perspective, and therefore could be of global significance.

Subfields: Economics and Econometrics, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/4fc2141b13bac52770fc40b7b839b830cb55cbdd

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

  1. Reinout Heijungs
  2. Kai Fang
  3. Kuishuang Feng
  4. Xianjin Huang
  5. D. Guan

In Environmental Engineering

  1. Yanmei Li
  2. Reinout Heijungs
  3. Kai Fang
  4. Qinli Lu
  5. Kuishuang Feng
  6. Xianjin Huang
  7. D. Guan
  8. André Bardow

Citations in the corpus