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Low-carbon benefits of aircraft adopting continuous descent operations

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  • Xue, Dabin
  • Du, Sen
  • Wang, Bing
  • Shang, Wen-Long
  • Avogadro, Nicolò
  • Ochieng, Washington Yotto

Abstract

Continuous descent operations (CDOs) can significantly reduce fuel consumption compared to the conventional step-down approaches by minimizing level-off segments and enabling engines to operate at idle or near-idle during descent. However, accurately quantifying the potential environmental benefits of CDOs is empirically challenging and depends on factors such as airspace structure, traffic density, and aircraft performance. To address this challenge, we propose a straightforward method to estimate the benefits of applying CDOs. We validate the proposed approach by leveraging quick access recorder data from movements at seven major airports in China. The results show that CDOs can reduce fuel consumption by an average of 139 kg per flight, decreasing CO2 and other emissions during the descent phase. This can contribute to improving air quality around airports. Looking forward, we estimate that the nationwide adoption of CDOs in China could cumulatively reduce CO2 emissions by approximately 67.6 Mt. in the period 2025–2050 considering air traffic demand forecasts and expected technological advancements. Overall, this study highlights the critical role of CDOs in promoting sustainable aviation, providing a robust basis for policymakers to support the global adoption of CDOs for the net-zero transition of the aviation industry.

Suggested Citation

  • Xue, Dabin & Du, Sen & Wang, Bing & Shang, Wen-Long & Avogadro, Nicolò & Ochieng, Washington Yotto, 2025. "Low-carbon benefits of aircraft adopting continuous descent operations," Applied Energy, Elsevier, vol. 383(C).
  • Handle: RePEc:eee:appene:v:383:y:2025:i:c:s0306261925001205
    DOI: 10.1016/j.apenergy.2025.125390
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    1. Shang, Wen-Long & Song, Xuewang & Xiang, Qiannian & Chen, Haibo & Elhajj, Mireille & Bi, Huibo & Wang, Kun & Ochieng, Washington, 2025. "The impact of deep reinforcement learning-based traffic signal control on Emission reduction in urban Road networks empowered by cooperative vehicle-infrastructure systems," Applied Energy, Elsevier, vol. 390(C).
    2. Zou, Runyuan & Wang, Bing & Wang, Kun & Shang, Wen-Long & Xue, Dabin & Ochieng, Washington Yotto, 2025. "A pathway to sustainable aviation: Modeling aircraft takeoff mass for precise fuel consumption and aircraft emission calculations," Energy, Elsevier, vol. 319(C).

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