LAPSE:2023.2039
Published Article

LAPSE:2023.2039
A Long-Term Decarbonisation Modelling and Optimisation Approach for Transport Sector Planning Considering Modal Shift and Infrastructure Construction: A Case Study of China
February 21, 2023
Abstract
Reducing direct carbon emissions in the transport sector is crucial for carbon neutrality. It is a considerable challenge to achieve substantial CO2 emissions reductions while satisfying rapidly growing traffic demands. Previous studies cannot be applied directly in long-term planning for the transport sector with rapid demand growth. To bridge this gap, a multi-regional model is proposed in this paper to quantify the optimal decarbonisation path for the transport sector in order to save costs. Considering modal shift and infrastructure construction, this model regards the transport sector as a whole and China is taken as a case study. The results show that electricity and hydrogen will be the major fuels of the transport sector in the future, accounting for 45 percent and 25 percent of fuel demands in 2060. This means that the electricity used by the transport sector accounts for 10 percent of the electricity consumed by the whole of society. The results reflect that freight transport has reached a CO2 emissions peak, while passenger transport will reach its own CO2 emissions peak around 2041. Giving priority to decarbonisation in freight transport can save 5 percent of the transition cost. The results also suggest that modal shift can save at most 7 percent of the transition cost.
Reducing direct carbon emissions in the transport sector is crucial for carbon neutrality. It is a considerable challenge to achieve substantial CO2 emissions reductions while satisfying rapidly growing traffic demands. Previous studies cannot be applied directly in long-term planning for the transport sector with rapid demand growth. To bridge this gap, a multi-regional model is proposed in this paper to quantify the optimal decarbonisation path for the transport sector in order to save costs. Considering modal shift and infrastructure construction, this model regards the transport sector as a whole and China is taken as a case study. The results show that electricity and hydrogen will be the major fuels of the transport sector in the future, accounting for 45 percent and 25 percent of fuel demands in 2060. This means that the electricity used by the transport sector accounts for 10 percent of the electricity consumed by the whole of society. The results reflect that freight transport has reached a CO2 emissions peak, while passenger transport will reach its own CO2 emissions peak around 2041. Giving priority to decarbonisation in freight transport can save 5 percent of the transition cost. The results also suggest that modal shift can save at most 7 percent of the transition cost.
Record ID
Keywords
infrastructure, modal shift, optimisation, systematic analysis, transport decarbonisation
Subject
Suggested Citation
Li C, Liu P, Li Z. A Long-Term Decarbonisation Modelling and Optimisation Approach for Transport Sector Planning Considering Modal Shift and Infrastructure Construction: A Case Study of China. (2023). LAPSE:2023.2039
Author Affiliations
Li C: State Key Lab of Power Systems, Department of Energy and Power Engineering, Tsinghua-BP Clean Energy Center, Tsinghua University, Beijing 100084, China
Liu P: State Key Lab of Power Systems, Department of Energy and Power Engineering, Tsinghua-BP Clean Energy Center, Tsinghua University, Beijing 100084, China
Li Z: State Key Lab of Power Systems, Department of Energy and Power Engineering, Tsinghua-BP Clean Energy Center, Tsinghua University, Beijing 100084, China
Liu P: State Key Lab of Power Systems, Department of Energy and Power Engineering, Tsinghua-BP Clean Energy Center, Tsinghua University, Beijing 100084, China
Li Z: State Key Lab of Power Systems, Department of Energy and Power Engineering, Tsinghua-BP Clean Energy Center, Tsinghua University, Beijing 100084, China
Journal Name
Processes
Volume
10
Issue
7
First Page
1371
Year
2022
Publication Date
2022-07-13
ISSN
2227-9717
Version Comments
Original Submission
Other Meta
PII: pr10071371, Publication Type: Journal Article
Record Map
Published Article

LAPSE:2023.2039
This Record
External Link

https://doi.org/10.3390/pr10071371
Publisher Version
Download
Meta
Record Statistics
Record Views
246
Version History
[v1] (Original Submission)
Feb 21, 2023
Verified by curator on
Feb 21, 2023
This Version Number
v1
Citations
Most Recent
This Version
URL Here
https://psecommunity.org/LAPSE:2023.2039
Record Owner
Auto Uploader for LAPSE
Links to Related Works
[0.21 s]
