IDEAS home Printed from https://ideas.repec.org/a/eee/lauspo/v169y2026ics026483772600205x.html

Structural adjustment of industrial land use and economic resilience: Evidence from China's poverty-stricken counties

Author

Listed:
  • Liu, Jingtong
  • Zhou, Dingyang
  • Jiang, Guanghui

Abstract

The cultivation of economic resilience in poverty-stricken areas is often constrained by capital shortage and development path locking. While existing studies primarily examine resilience through industrial composition and upgrading, the role of industrial land use structure as a spatially embedded and institutionally mediated dimension of economic organization remains underexplored. Based on data from 756 Chinese poverty-stricken counties (2010–2020), this study employs the shift-share model and XGBoost-SHAP method to examine the nonlinear and heterogeneous relationships between structural adjustment of industrial land use and economic resilience. The adopted methodological framework further uncovers threshold effects and nonlinear patterns that are difficult to detect using linear approaches. The results exhibit pronounced heterogeneity across land use types: a high concentration of resource-intensive industrial land is associated with lower economic resilience, reflecting potential technology lock-in; adjustments in capital-intensive industrial land exhibit an inverted U-shaped relationship with resilience; and higher shares of labor- and technology-intensive industrial land are consistently associated with stronger resilience, likely through employment expansion and knowledge spillovers. These findings demonstrate that industrial land use structure provides a complementary analytical lens by linking land allocation, factor mobility, and development path dependence. By highlighting the differentiated and policy-conditioned associations between industrial land use structure and economic resilience, this study offers insights into resilience-building strategies for poverty-stricken regions.

Suggested Citation

  • Liu, Jingtong & Zhou, Dingyang & Jiang, Guanghui, 2026. "Structural adjustment of industrial land use and economic resilience: Evidence from China's poverty-stricken counties," Land Use Policy, Elsevier, vol. 169(C).
    • Handle: RePEc:eee:lauspo:v:169:y:2026:i:c:s026483772600205x
    DOI: 10.1016/j.landusepol.2026.108121
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S026483772600205X
    Download Restriction: Full text may be restricted for ScienceDirect subscribers
    File URL: https://libkey.io/10.1016/j.landusepol.2026.108121?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. David M. Blei & Alp Kucukelbir & Jon D. McAuliffe, 2017. "Variational Inference: A Review for Statisticians," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(518), pages 859-877, April.
    2. Ron Martin, 2012. "Regional economic resilience, hysteresis and recessionary shocks," Journal of Economic Geography, Oxford University Press, vol. 12(1), pages 1-32, January.
    3. Arthur, W Brian, 1989. "Competing Technologies, Increasing Returns, and Lock-In by Historical Events," Economic Journal, Royal Economic Society, vol. 99(394), pages 116-131, March.
    4. Mingdou Zhang & Qingbang Wu & Weilu Li & Dongqi Sun & Fei Huang, 2021. "Intensifier of urban economic resilience: Specialized or diversified agglomeration?," PLOS ONE, Public Library of Science, vol. 16(11), pages 1-21, November.
    5. Franciska T. de Vries & Mira E. Liiri & Lisa Bjørnlund & Matthew A. Bowker & Søren Christensen & Heikki M. Setälä & Richard D. Bardgett, 2012. "Land use alters the resistance and resilience of soil food webs to drought," Nature Climate Change, Nature, vol. 2(4), pages 276-280, April.
    6. Susan Athey & Guido W. Imbens, 2019. "Machine Learning Methods That Economists Should Know About," Annual Review of Economics, Annual Reviews, vol. 11(1), pages 685-725, August.
    7. Yujie Huang & Xianke Huang & Ruiliang Li & Wei Cheng, 2023. "Assessing the effectiveness of targeted poverty alleviation policies in Xinjiang, China," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 10(1), pages 1-11, December.
    8. Christian Hundt & Linus Holtermann, 2020. "The role of national settings in the economic resilience of regions—Evidence from recessionary shocks in Europe from 1990 to 2014," Growth and Change, Wiley Blackwell, vol. 51(1), pages 180-206, March.
    9. Lianjie Qin & Laiyin Zhu & Baoyin Liu & Zixuan Li & Yugang Tian & Gordon Mitchell & Shifei Shen & Wei Xu & Jianguo Chen, 2024. "Global expansion of tropical cyclone precipitation footprint," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
    10. Liu, Jingtong & Zhou, Dingyang & Jiang, Guanghui, 2025. "The implementation of China’s poverty alleviation policies has enhanced the diversity of the industrial structure," Economic Analysis and Policy, Elsevier, vol. 87(C), pages 446-462.
    11. Min Kim & Soojin You & Jinhyung Chon & Junga Lee, 2017. "Sustainable Land-Use Planning to Improve the Coastal Resilience of the Social-Ecological Landscape," Sustainability, MDPI, vol. 9(7), pages 1-21, June.
    12. Athey, Susan & Imbens, Guido W., 2019. "Machine Learning Methods Economists Should Know About," Research Papers 3776, Stanford University, Graduate School of Business.
    13. Ron Martin & Peter Sunley, 2020. "Regional economic resilience: evolution and evaluation," Chapters, in: Gillian Bristow & Adrian Healy (ed.), Handbook on Regional Economic Resilience, chapter 2, pages 10-35, Edward Elgar Publishing.
    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. Igor Sadoune & Marcelin Joanis & Andrea Lodi, 2025. "Implementing a Hierarchical Deep Learning Approach for Simulating Multilevel Auction Data," Computational Economics, Springer;Society for Computational Economics, vol. 65(4), pages 2029-2056, April.
    2. Sophie-Charlotte Klose & Johannes Lederer, 2020. "A Pipeline for Variable Selection and False Discovery Rate Control With an Application in Labor Economics," Papers 2006.12296, arXiv.org, revised Jun 2020.
    3. Kyle Colangelo & Ying-Ying Lee, 2019. "Double debiased machine learning nonparametric inference with continuous treatments," CeMMAP working papers CWP72/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    4. Labib Shami & Teddy Lazebnik, 2024. "Implementing Machine Learning Methods in Estimating the Size of the Non-observed Economy," Computational Economics, Springer;Society for Computational Economics, vol. 63(4), pages 1459-1476, April.
    5. Hurmeranta, Risto & Lyytikäinen, Teemu, 2025. "Nominal Loss Aversion in the Housing Market and Household Mobility," Working Papers 178, VATT Institute for Economic Research.
    6. Chen, Ruoyu & Jiang, Hanchen & Quintero, Luis E., 2023. "Measuring the value of rent stabilization and understanding its implications for racial inequality: Evidence from New York City," Regional Science and Urban Economics, Elsevier, vol. 103(C).
    7. Dang, Hai-Anh & Carleto, Gero & Gourlay, Sydney & Abanokova, Kseniya, 2023. "Addressing Soil Quality Data Gaps with Imputation: Evidence from Ethiopia and Uganda," 2023 Annual Meeting, July 23-25, Washington D.C. 335648, Agricultural and Applied Economics Association.
    8. Dangxing Chen & Luyao Zhang, 2023. "Monotonicity for AI ethics and society: An empirical study of the monotonic neural additive model in criminology, education, health care, and finance," Papers 2301.07060, arXiv.org.
    9. Ballestar, María Teresa & Mir, Miguel Cuerdo & Pedrera, Luis Miguel Doncel & Sainz, Jorge, 2024. "Effectiveness of tutoring at school: A machine learning evaluation," Technological Forecasting and Social Change, Elsevier, vol. 199(C).
    10. Giorgio Chiovelli & Stelios Michalopoulus & Elias Papaioannou & Tanner Regan, 2025. "Illuminating the Global South," Working Papers 2025-009, The George Washington University, The Center for Economic Research.
    11. Combes, Pierre-Philippe & Gobillon, Laurent & Zylberberg, Yanos, 2022. "Urban economics in a historical perspective: Recovering data with machine learning," Regional Science and Urban Economics, Elsevier, vol. 94(C).
    12. Barzin,Samira & Avner,Paolo & Maruyama Rentschler,Jun Erik & O’Clery,Neave, 2022. "Where Are All the Jobs ? A Machine Learning Approach for High Resolution Urban Employment Prediction inDeveloping Countries," Policy Research Working Paper Series 9979, The World Bank.
    13. Arenas, Andreu & Calsamiglia, Caterina, 2022. "Gender Differences in High-Stakes Performance and College Admission Policies," IZA Discussion Papers 15550, IZA Network @ LISER.
    14. Tsang, Andrew, 2021. "Uncovering Heterogeneous Regional Impacts of Chinese Monetary Policy," MPRA Paper 110703, University Library of Munich, Germany.
    15. Kyle Colangelo & Ying-Ying Lee, 2019. "Double debiased machine learning nonparametric inference with continuous treatments," CeMMAP working papers CWP54/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    16. Slonimczyk, Fabian, 2025. "This Candidate is [MASK]. Prompt-based Sentiment Extraction and Reference Letters," MPRA Paper 126675, University Library of Munich, Germany.
    17. Daniel Goller, 2023. "Analysing a built-in advantage in asymmetric darts contests using causal machine learning," Annals of Operations Research, Springer, vol. 325(1), pages 649-679, June.
    18. Rama K. Malladi, 2024. "Benchmark Analysis of Machine Learning Methods to Forecast the U.S. Annual Inflation Rate During a High-Decile Inflation Period," Computational Economics, Springer;Society for Computational Economics, vol. 64(1), pages 335-375, July.
    19. Tranos, Emmanouil & Incera, Andre Carrascal & Willis, George, 2022. "Using the web to predict regional trade flows: data extraction, modelling, and validation," OSF Preprints 9bu5z, Center for Open Science.

    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:lauspo:v:169:y:2026:i:c:s026483772600205x. 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: Joice Jiang (email available below). General contact details of provider: https://www.journals.elsevier.com/land-use-policy .

    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.