Author
Listed:
- Yutang Liu
(Beijing Jiaotong University)
- Anqiang Huang
(Beijing Jiaotong University)
- Zhou Yao
(Beijing Jiaotong University)
Abstract
China’s energy structure is undergoing significant changes as a result of the quick development of new energy technologies. Building and enhancing the carbon emission policy system for China’s transportation industry requires scientific prediction of carbon emissions with the aim of carbon peaking. This paper adopts the LMDI (Logarithmic Mean Divisia Index) method to decompose the driving factors and the LEAP (Long-Range Energy Alternatives Planning) model to predict the trajectory of China’s transportation sector’s carbon emissions. The findings indicate that while an increase in GDP per capita will result in an increase in carbon emissions, an improvement in the energy structure and a decrease in the intensity of transportation will contribute to a reduction. Additionally, the boosting effect of GDP per capita on carbon emissions in 2019 has been able to counteract the inhibiting effects of energy structure and transportation intensity, where road freight, which is primarily powered by diesel, is the largest source of carbon emissions. According to the baseline scenario, the first carbon peak will occur in 2023 due to the liberalization of epidemic prevention and control and the decrease in the share of road freight, while the second peak will happen in 2032 due to the combined effects of economic growth and advancements in carbon reduction technology. As a result, carbon emissions will exhibit a fluctuating peak tendency, indicating that the industry should pay attention to these features and adopt scientific development strategies. Therefore, the primary routes for China’s transportation sector to reach a carbon peak in the future include optimizing energy structure, developing carbon-reducing technologies, lowering the proportion of road freight, encouraging multi-modal transport development, and optimizing transport structure.
Suggested Citation
Yutang Liu & Anqiang Huang & Zhou Yao, 2024.
"Carbon Emissions Prediction for the Transportation Industry with Consideration of China’s Peaking Carbon Emissions,"
Lecture Notes in Operations Research, in: Daqing Gong & Yixuan Ma & Xiaowen Fu & Juliang Zhang & Xiaopu Shang (ed.), Liss 2023, pages 406-426,
Springer.
Handle:
RePEc:spr:lnopch:978-981-97-4045-1_32
DOI: 10.1007/978-981-97-4045-1_32
Download full text from publisher
To our knowledge, this item is not available for
download. To find whether it is available, there are three
options:
1. Check below whether another version of this item is available online.
2. Check on the provider's
web page
whether it is in fact available.
3. Perform a
for a similarly titled item that would be
available.
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:lnopch:978-981-97-4045-1_32. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
We have no bibliographic references for this item. You can help adding them by using this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .
Please note that corrections may take a couple of weeks to filter through
the various RePEc services.