Mapping carbon emissions of China's domestic air passenger transport: From individual cities to intercity networks

Li, Fangyi; Li, Fēi; Cai, Bofeng; Lv, Chen

doi:10.1016/j.scitotenv.2022.158199

Cited by 7 publications

(2 citation statements)

References 72 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In recent years, deep learning techniques have gradually attracted attention in engine fault diagnosis. Researchers have begun to explore the use of deep neural networks for end-to-end learning and processing of multimodal data [24][25][26][27][28] . For example, image feature extraction using convolutional neural networks (CNNs), processing time series data using recurrent neural networks (RNNs), and then fusing features from different modal data to achieve more accurate engine fault classification and diagnosis.…”

Section: Related Workmentioning

confidence: 99%

Enhancing aero-engine airway fault diagnosis through multimodal deep neural networks

Yang,

Lin,

Guo

et al. 2024

Second International Conference on Physics, Photonics, and Optical Engineering (ICPPOE 2023)

View full text Add to dashboard Cite

This research delves into an innovative approach for diagnosing faults in aero-engine airways using a multimodal deep neural network. The method involves creating a sophisticated neural network model capable of precisely detecting and categorizing various airway faults within the engine. This is achieved by synergistically integrating data from multiple sensory modalities, such as sound and vibration, to enhance the accuracy of fault identification and classification. The study conducted in-depth analysis and optimisation in data preprocessing, feature extraction and model design to improve the performance of the diagnostic model. Experimental results show that the proposed multimodal deep neural network method exhibits high accuracy and reliability in the diagnosis of aero-engine airway faults, and has potential for practical application.

show abstract

Section: Related Workmentioning

confidence: 99%

Enhancing aero-engine airway fault diagnosis through multimodal deep neural networks

Yang,

Lin,

Guo

et al. 2024

Second International Conference on Physics, Photonics, and Optical Engineering (ICPPOE 2023)

View full text Add to dashboard Cite

show abstract

“…Applying the same method, Li et al (2022) created an urban aviation carbon emissions network. They found the Shanghai-Beijing, Beijing-Shenzhen, and Beijing-Guangzhou routes had the highest carbon outputs [29]. Zhou et al (2016) used simulations to suggest that without major tech advances, China's civil aviation might not reach carbon neutrality by 2030 [20].…”

Section: Introductionmentioning

confidence: 99%

From Takeoff to Touchdown: A Decade’s Review of Carbon Emissions from Civil Aviation in China’s Expanding Megacities

She,

Deng,

Chen

2023

Sustainability

View full text Add to dashboard Cite

The rapid growth of urbanization in China has led to a substantial escalation in the demand for civil aviation services, consequently propelling China to the third-largest contributor of carbon emissions within the aviation sector. Using the 2012–2021 data on takeoffs and landings of civil aviation aircraft in China, the aircraft engine emission factor database of the Base of Aircraft Data (BADA) from EUROCONTROL, this paper investigates the spatial-temporal distribution characteristics of atmospheric pollutants, primarily carbon emissions from Chinese civil aviation aircraft in 19 megacities. The results indicate that (1) China’s aviation CO2 emissions equivalent between 2012 and 2022 has been on an upward trajectory, peaking at 186.53 MT in 2019 with an average annual growth of 12.52%. The trend, albeit momentarily interrupted by the COVID-19 pandemic, appears to persist. (2) CO2 constitutes the highest proportion of aircraft emissions at 83.87%, with Cruise Climb Descent (CCD) cycle emissions accounting for 96.24%. CO2 and NOX, with the highest increase rates in the CCD and Landing and Takeoff (LTO) phases, respectively, are identified as the chief culprits in aviation-related greenhouse effects. (3) There is a marked spatial imbalance, with 19 megacities contributing 62.08% of total CO2 emissions, compared to the 207 least-emitting cities contributing just 9.29%. (4) The pattern of city carbon emissions is changing, with rapid growth rates in the western cities of Xinjiang, Tibet, Shaanxi, and Guizhou, and varied growth rates among megacities. The implications of this study emphasize the urgency for advancements in aviation fuel technology, rigorous management of CCD phase pollutants, strategic carbon emission controls in populous cities, fostering green aviation initiatives in western regions, diverse carbon mitigation tactics, and strengthening the precision and surveillance of aviation carbon accounting systems. Collectively, this study paints a grand picture of the complexities and challenges associated with China’s urban sprawl and aviation carbon emissions.

show abstract

Analysis of spatial correlation networks of carbon emissions in emerging economies

Zhang

Yao²

2023

Environ Sci Pollut Res

View full text Add to dashboard Cite

Mapping carbon emissions of China's domestic air passenger transport: From individual cities to intercity networks

Cited by 7 publications

References 72 publications

Enhancing aero-engine airway fault diagnosis through multimodal deep neural networks

Enhancing aero-engine airway fault diagnosis through multimodal deep neural networks

From Takeoff to Touchdown: A Decade’s Review of Carbon Emissions from Civil Aviation in China’s Expanding Megacities

Analysis of spatial correlation networks of carbon emissions in emerging economies

Contact Info

Product

Resources

About