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CO2 emission change in China's aviation industry: A fleet-wide index decomposition and scenario analysis
A fleet-wide index decomposition and scenario analysis model is developed to identify the influencing factors of CO2 emission change in China's aviation industry and predict CO2 emissions in multiple emission reduction scenarios through 2040. It was discovered that all aircraft types experienced operational improvements during 2009 and 2019. The fleet utilization growth resulted in the most of CO2 emission reduction. However, slowdown in fleet updates existed for most of aircraft types, and the decreased fleet fuel intensity only resulted in CO2 emission reduction in specific years. The rising air transport demand continues to be the greatest obstacle to reducing emissions. Under the baseline scenario, aviation carbon intensity can only realize the 60–65% target by 2035. With endeavours on traffic demand control and technology, aviation CO2 emissions will peak at 123 Mt in 2035. The rapid sustainable alternative fuels substitutions will be essential for achieving China's 35–40% target before 2040.
Funding
National Natural Science Foundation of China (No. 52270183)
Interdisciplinary Innovation Foundation for Graduates of NUAA (No. KXKCXJJ202001)
China Scholarship Council (No.701(2021))
National Natural Science Foundation of China (No. 72064005)
History
School
- Loughborough University London
Published in
Transportation Research Part D: Transport and EnvironmentVolume
119Issue
2023Publisher
ElsevierVersion
- AM (Accepted Manuscript)
Rights holder
© ElsevierPublisher statement
This paper was accepted for publication in Transportation Research Part D: Transport and Environment published by Elsevier. The final publication is available at https://doi.org/10.1016/j.trd.2023.103743. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/Acceptance date
2023-04-12Publication date
2023-05-03Copyright date
2023ISSN
1361-9209eISSN
1879-2340Publisher version
Language
- en