IDEAS home Printed from https://ideas.repec.org/a/eee/transa/v200y2025ics0965856425002885.html

The impact of low-altitude airspace opening policy on aviation manufacturing innovation: A double machine learning approach

Author

Listed:
  • Zhao, Linan
  • Chen, Yanru
  • Wahab, M.I.M.

Abstract

Since 2010, China has been reforming low-altitude airspace management and has implemented the low-altitude airspace opening policy aimed at reducing the restrictions on low-altitude airspace and stimulating the development of the low-altitude economy. To explore the implications of the low-altitude airspace opening policy (LAAOP) and promote innovation development in enterprises, this paper employs double machine learning-based causal inference methods to examine the impact of the policy on aviation manufacturing industry innovation, using data from listed Chinese aviation manufacturing firms between 2003 and 2023. The results indicate that the policy has enhanced the innovation capacity of the sector. The conclusion is robustly validated through both Difference-in-Differences (DiD) and Generalized Random Forest (GRF) models. Heterogeneity analysis reveals that the policy exerts a stronger positive effect on the drone enterprises, state-owned enterprises, and coastal region enterprises. Accordingly, this paper offers a series of policy recommendations to optimize policy implementation and promote high-quality development within China’s aviation manufacturing industry.

Suggested Citation

  • Zhao, Linan & Chen, Yanru & Wahab, M.I.M., 2025. "The impact of low-altitude airspace opening policy on aviation manufacturing innovation: A double machine learning approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 200(C).
  • Handle: RePEc:eee:transa:v:200:y:2025:i:c:s0965856425002885
    DOI: 10.1016/j.tra.2025.104660
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0965856425002885
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.tra.2025.104660?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Askerbekov, Dauren & Garza-Reyes, Jose Arturo & Roy Ghatak, Ranjit & Joshi, Rohit & Kandasamy, Jayakrishna & Luiz de Mattos Nascimento, Daniel, 2024. "Embracing drones and the Internet of drones systems in manufacturing – An exploration of obstacles," Technology in Society, Elsevier, vol. 78(C).
    2. Safadi, Yazan & Geroliminis, Nikolas & Haddad, Jack, 2024. "Integrated departure and boundary control for low-altitude air city transport systems," Transportation Research Part B: Methodological, Elsevier, vol. 189(C).
    3. Hao, Yanjin & Peng, Binbin & Zou, Hongyang & Zhu, Ning & Du, Huibin, 2025. "How does differentiated subsidy adjustment influence new energy vehicle sales?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 195(C).
    4. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2018. "Double/debiased machine learning for treatment and structural parameters," Econometrics Journal, Royal Economic Society, vol. 21(1), pages 1-68, February.
    5. Christopher R. Knittel & Samuel Stolper, 2021. "Machine Learning about Treatment Effect Heterogeneity: The Case of Household Energy Use," AEA Papers and Proceedings, American Economic Association, vol. 111, pages 440-444, May.
    6. Paul B. Ellickson & Wreetabrata Kar & James C. Reeder, 2023. "Estimating Marketing Component Effects: Double Machine Learning from Targeted Digital Promotions," Marketing Science, INFORMS, vol. 42(4), pages 704-728, July.
    7. Guiming Liao & Zheli Hu & Zhichen Yang & Qianlin Yin & Jiahui Chen, 2025. "The Perception of Policy Uncertainty and the Labor Income Share of Firms: Empirical Research Based on Double Machine Learning," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 46(2), pages 999-1011, March.
    8. Yin, Hua & Yin, Xieyu & Wen, Fenghua, 2025. "Artificial intelligence and climate risk: A double machine learning approach," International Review of Financial Analysis, Elsevier, vol. 103(C).
    9. Ren, Xinhui & Wang, Jiarui, 2025. "Symbiotic evolution mechanism of urban air mobility industrial innovation ecosystem: Evidence from low altitude air mobility in Shenzhen," Journal of Air Transport Management, Elsevier, vol. 124(C).
    10. Christine Blandhol & John Bonney & Magne Mogstad & Alexander Torgovitsky, 2022. "When is TSLS Actually LATE?," NBER Working Papers 29709, National Bureau of Economic Research, Inc.
    11. Malik, Mahfuja & Mamun, Khawaja & Osman, Syed Muhammad Ishraque, 2025. "Does corruption control enhance ESG-induced firm value? Insights from machine learning analysis," Finance Research Letters, Elsevier, vol. 72(C).
    12. Xing, Lu & Han, DongHao & Hui, Xie, 2023. "The impact of carbon policy on corporate risk-taking with a double/debiased machine learning based difference-in-differences approach," Finance Research Letters, Elsevier, vol. 58(PC).
    13. Jiang, Yirui & Tran, Trung Hieu & Williams, Leon, 2023. "Machine learning and mixed reality for smart aviation: Applications and challenges," Journal of Air Transport Management, Elsevier, vol. 111(C).
    14. Oh, Soohwan & Yoon, Yoonjin, 2024. "Urban drone operations: A data-centric and comprehensive assessment of urban airspace with a Pareto-based approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 182(C).
    15. Hunjra, Ahmed Imran & Zhao, Shikuan & Goodell, John W. & Liu, Xiaoqian, 2024. "Digital economy policy and corporate low-carbon innovation: Evidence from a quasi-natural experiment in China," Finance Research Letters, Elsevier, vol. 60(C).
    16. Jonathan M.V. Davis & Sara B. Heller, 2020. "Rethinking the Benefits of Youth Employment Programs: The Heterogeneous Effects of Summer Jobs," The Review of Economics and Statistics, MIT Press, vol. 102(4), pages 664-677, October.
    17. Saeed Mousa & Rabeh Morrar, 2023. "Impacts of Industry 4.0 on servitization of manufacturing [L’impact de l’industrie 4.0 sur la servitisation de l’industrie]," Post-Print hal-04030546, HAL.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Black, Dan A. & Grogger, Jeffrey & Kirchmaier, Tom & Sanders, Koen, 2023. "Criminal charges, risk assessment and violent recidivism in cases of domestic abuse," LSE Research Online Documents on Economics 121374, London School of Economics and Political Science, LSE Library.
    2. Chen, Jianbao & Shen, Jiamin & Ke, Nan, 2025. "Assessing the impact of new energy demonstration city policy on industrial carbon intensity using machine learning," Economic Analysis and Policy, Elsevier, vol. 87(C), pages 1690-1707.
    3. Wang, Weilong & Wang, Jianlong & Wu, Haitao, 2024. "The impact of energy-consuming rights trading on green total factor productivity in the context of digital economy: Evidence from listed firms in China," Energy Economics, Elsevier, vol. 131(C).
    4. Mark Kattenberg & Bas Scheer & Jurre Thiel, 2023. "Causal forests with fixed effects for treatment effect heterogeneity in difference-in-differences," CPB Discussion Paper 452, CPB Netherlands Bureau for Economic Policy Analysis.
    5. Lu, Heyu & Li, Jiayang & Wu, Zongfa & Zeng, Yufeiyang, 2025. "Rethinking energy allocation: Can green finance be the solution? — Evidence from machine learning method," Energy, Elsevier, vol. 332(C).
    6. Lüthi, Samuel, 2025. "Classroom versus workbench: The impact of firm-based learning on labour market and educational outcomes," Economics of Education Review, Elsevier, vol. 109(C).
    7. Phillip Heiler & Michael C. Knaus, 2021. "Effect or Treatment Heterogeneity? Policy Evaluation with Aggregated and Disaggregated Treatments," Papers 2110.01427, arXiv.org, revised Aug 2023.
    8. Victor Chernozhukov & Mert Demirer & Esther Duflo & Iván Fernández‐Val, 2025. "Fisher–Schultz Lecture: Generic Machine Learning Inference on Heterogeneous Treatment Effects in Randomized Experiments, With an Application to Immunization in India," Econometrica, Econometric Society, vol. 93(4), pages 1121-1164, July.
    9. Goller, Daniel & Lechner, Michael & Pongratz, Tamara & Wolff, Joachim, 2025. "Active labor market policies for the long-term unemployed: New evidence from causal machine learning," Labour Economics, Elsevier, vol. 94(C).
    10. Yang, Jiayu & Wang, Jianlong & Wang, Weilong & Wu, Haitao, 2024. "Exploring the path to promote energy revolution: Assessing the impact of new energy demonstration city construction on urban energy transition in China," Renewable Energy, Elsevier, vol. 236(C).
    11. Günter J. Hitsch & Sanjog Misra & Walter W. Zhang, 2024. "Heterogeneous treatment effects and optimal targeting policy evaluation," Quantitative Marketing and Economics (QME), Springer, vol. 22(2), pages 115-168, June.
    12. Luis Antonio Fantozzi Alvarez & Rodrigo Toneto, 2024. "The interpretation of 2SLS with a continuous instrument: a weighted LATE representation," Working Papers, Department of Economics 2024_11, University of São Paulo (FEA-USP).
    13. Victor Chernozhukov & Mert Demirer & Esther Duflo & Ivan Fernandez-Val, 2017. "Generic machine learning inference on heterogenous treatment effects in randomized experiments," CeMMAP working papers 61/17, Institute for Fiscal Studies.
    14. Mogstad, Magne & Torgovitsky, Alexander, 2024. "Instrumental variables with unobserved heterogeneity in treatment effects," Handbook of Labor Economics,, Elsevier.
    15. Alejandro Sanchez-Becerra, 2023. "Robust inference for the treatment effect variance in experiments using machine learning," Papers 2306.03363, arXiv.org.
    16. Zhang, Tingyu & Pu, Zhengning, 2025. "Can energy-consuming rights trading promote green continuous innovation in enterprises? The moderating role of digitization," Energy Economics, Elsevier, vol. 149(C).
    17. Achim Ahrens & Alessandra Stampi‐Bombelli & Selina Kurer & Dominik Hangartner, 2024. "Optimal multi‐action treatment allocation: A two‐phase field experiment to boost immigrant naturalization," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(7), pages 1379-1395, November.
    18. Zhang, Shengwu & Han, Liyan, 2026. "Low-altitude infrastructure and economic growth: Evidence from general aviation airports," Transport Policy, Elsevier, vol. 175(C).
    19. Pedro Forquesato, 2022. "Who Benefits from Political Connections in Brazilian Municipalities," Papers 2204.09450, arXiv.org.
    20. Olga Takács & János Vincze, 2023. "Where is the pain the most acute? The market segments particularly affected by gender wage discrimination in Hungary," KRTK-KTI WORKING PAPERS 2304, Institute of Economics, Centre for Economic and Regional Studies.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:eee:transa:v:200:y:2025:i:c:s0965856425002885. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/547/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.