IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v17y2025i8p3348-d1631209.html
   My bibliography  Save this article

Empowered or Negative? Research on the Impact of Industrial Agglomeration on the Development of Agricultural New Quality Productive Forces: Evidence from Shandong Province, China

Author

Listed:
  • Shoulin Li

    (College of Economics and Management, Qingdao Agricultural University, Qingdao 266109, China)

  • Jianing Liu

    (College of Economics and Management, Qingdao Agricultural University, Qingdao 266109, China)

  • Weiya Guo

    (College of Economics and Management, Qingdao Agricultural University, Qingdao 266109, China)

Abstract

Realizing the SDGs is a core issue of global development. In this regard, China has put forward a new quality productive forces development path with innovative thinking, providing systematic solutions for sustainable transformation through factor allocation optimization and whole-chain innovation drive. In the agricultural sector, industrial agglomeration is one of the factors affecting the development of new quality productive forces, with a spatial layout that can improve the efficiency of agricultural production and the effective utilization of resources. This paper investigates the impact of agricultural industry agglomeration on new quality productive forces by using the spatial Durbin model (SDM) to measure the relevant data of 16 prefecture-level cities in Shandong, China, from 2010 to 2022. The results show the following: (1) The spatial patterns of agricultural industry agglomeration and new quality productive forces in Shandong Province have been evolving, showing an obvious spatial correlation and “high in the south and low in the north” and “high in the north and low in the south” spatial patterns, respectively. (2) From a global perspective, industrial agglomeration has significant negative direct and indirect effects on the development of agricultural new quality productive forces, and this conclusion still holds after robustness testing. (3) From a local perspective, the impact of agricultural industry agglomeration on new quality productive forces is regionally heterogeneous. In the central economic zone, the impact is positive, while in the western and eastern economic zones, it is negative. This research provides a theoretical basis for optimizing the spatial layout of the agricultural industry and constructing a sustainable productivity system.

Suggested Citation

  • Shoulin Li & Jianing Liu & Weiya Guo, 2025. "Empowered or Negative? Research on the Impact of Industrial Agglomeration on the Development of Agricultural New Quality Productive Forces: Evidence from Shandong Province, China," Sustainability, MDPI, vol. 17(8), pages 1-23, April.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:8:p:3348-:d:1631209
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/17/8/3348/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/17/8/3348/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Lee, Lung-fei & Yu, Jihai, 2010. "Estimation of spatial autoregressive panel data models with fixed effects," Journal of Econometrics, Elsevier, vol. 154(2), pages 165-185, February.
    2. Yani Dong & Chunjie Qi & Yumeng Gu & Cheng Gui & Guozhu Fang, 2024. "Citrus Industry Agglomeration and Citrus Green Total Factor Productivity in China: An Empirical Analysis Utilizing a Dynamic Spatial Durbin Model," Agriculture, MDPI, vol. 14(11), pages 1-22, November.
    3. Cerina, Fabio & Mureddu, Francesco, 2014. "Is agglomeration really good for growth? Global efficiency, interregional equity and uneven growth," Journal of Urban Economics, Elsevier, vol. 84(C), pages 9-22.
    4. Jingruo Liu & Xiuju Feng & Jianxu Liu & Woraphon Yamaka, 2024. "Digital Economy and Industrial Structure Transformation: Mechanisms for High-Quality Development in China’s Agriculture and Rural Areas," Agriculture, MDPI, vol. 14(10), pages 1-19, October.
    5. Wei Liu & Xu Yang & Jingxiao Zhang & Xiangli Wu & Luhe Wan, 2024. "The spatiotemporal evolution of the effect of industrial agglomeration on industrial green economic efficiency: empirical evidence from pollution-intensive industries in China," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 26(4), pages 9945-9972, April.
    6. Ruikuan Yao & Zhisheng Ma & Haitao Wu & Yifeng Xie, 2024. "Mechanism and Measurement of the Effects of Industrial Agglomeration on Agricultural Economic Resilience," Agriculture, MDPI, vol. 14(3), pages 1-18, February.
    7. Kang Pan & Rong Liu & Xiaowei Chen & Ying Huang, 2023. "How Does Industrial Agglomeration Affect Exports? Evidence from Chinese Province-Industry Panel Data," Sustainability, MDPI, vol. 15(13), pages 1-18, June.
    8. Pei Xu & Zehu Jin & Huan Tang, 2022. "Influence Paths and Spillover Effects of Agricultural Agglomeration on Agricultural Green Development," Sustainability, MDPI, vol. 14(10), pages 1-16, May.
    9. Yunsong Xu & Ruixun Wang & Shanfei Zhang, 2025. "Digital Economy, Green Innovation Efficiency, and New Quality Productive Forces: Empirical Evidence from Chinese Provincial Panel Data," Sustainability, MDPI, vol. 17(2), pages 1-27, January.
    10. Han Hao & Chenyang Liu & Ling Xin, 2023. "Measurement and Dynamic Trend Research on the Development Level of Rural Industry Integration in China," Agriculture, MDPI, vol. 13(12), pages 1-16, December.
    11. Qingmei Zeng & Bin Fan & Fuzeng Wang, 2024. "Spatial and Temporal Evolution of the Coupling of Industrial Agglomeration and Carbon Emission Efficiency—Evidence from China’s Animal Husbandry Industry," Sustainability, MDPI, vol. 16(23), pages 1-26, November.
    12. Chuang Li & Qingqing Liu & Qing Li & Hailing Wang, 2022. "Does Innovative Industrial Agglomeration Promote Environmentally-Friendly Development? Evidence from Chinese Prefecture-Level Cities," Sustainability, MDPI, vol. 14(20), pages 1-21, October.
    13. Qingqing Huang & Wenjing Guo & Yanfei Wang, 2024. "A Study of the Impact of New Quality Productive Forces on Agricultural Modernization: Empirical Evidence from China," Agriculture, MDPI, vol. 14(11), pages 1-21, October.
    14. Dayong Huang & Yangyang Zhu & Qiuyue Yu, 2022. "Spatial Spillover Effects of Agricultural Agglomeration on Agricultural Non-Point Source Pollution in the Yangtze River Basin," Sustainability, MDPI, vol. 14(24), pages 1-26, December.
    15. Song Xu & Jiating Wang & Zhisheng Peng, 2024. "Study on the Promotional Effect and Mechanism of New Quality Productive Forces on Green Development," Sustainability, MDPI, vol. 16(20), pages 1-25, October.
    16. Xianghao Zhao & Changjiang Xiong, 2022. "Spatial and Temporal Characteristics, Evolution Law and Improvement Path of China’s Animal Husbandry Production Pattern," Sustainability, MDPI, vol. 14(23), pages 1-18, November.
    17. Shuai Qin & Zheying Han & Hong Chen & Haokun Wang & Cheng Guo, 2022. "High-Quality Development of Chinese Agriculture under Factor Misallocation," IJERPH, MDPI, vol. 19(16), pages 1-20, August.
    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. Triki, Mohamed Bilel, 2019. "The Italian wage curve revisited: A local and spatial cointegration," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 55, pages 73-90.
    2. repec:rri:wpaper:201303 is not listed on IDEAS
    3. Cem Ertur & Antonio Musolesi, 2017. "Weak and Strong Cross‐Sectional Dependence: A Panel Data Analysis of International Technology Diffusion," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(3), pages 477-503, April.
    4. Esposito, Piero & Patriarca, Fabrizio & Salvati, Luca, 2018. "Tertiarization and land use change: The case of Italy," Economic Modelling, Elsevier, vol. 71(C), pages 80-86.
    5. Askarov, Zohid & Doucouliagos, Hristos, 2015. "Spatial aid spillovers during transition," European Journal of Political Economy, Elsevier, vol. 40(PA), pages 79-95.
    6. Atems, Bebonchu, 2013. "The spatial dynamics of growth and inequality: Evidence using U.S. county-level data," Economics Letters, Elsevier, vol. 118(1), pages 19-22.
    7. Li, Kunpeng & Lin, Wei, 2024. "Threshold spatial autoregressive model," Journal of Econometrics, Elsevier, vol. 244(1).
    8. Tan, Xiujie & Yan, Yaxue & Dong, Yuyang, 2022. "Peer effect in green credit induced green innovation: An empirical study from China's Green Credit Guidelines," Resources Policy, Elsevier, vol. 76(C).
    9. Zhonghua Cheng & Xiaowen Hu, 2023. "The effects of urbanization and urban sprawl on CO2 emissions in China," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(2), pages 1792-1808, February.
    10. Michele Aquaro & Natalia Bailey & M. Hashem Pesaran, 2015. "Quasi Maximum Likelihood Estimation of Spatial Models with Heterogeneous Coefficients," CESifo Working Paper Series 5428, CESifo.
    11. Maria Lucia Parrella & Giuseppina Albano & Cira Perna & Michele La Rocca, 2021. "Bootstrap joint prediction regions for sequences of missing values in spatio-temporal datasets," Computational Statistics, Springer, vol. 36(4), pages 2917-2938, December.
    12. Takeshi Aida, 2020. "Revisiting suicide rate during wartime: Evidence from the Sri Lankan civil war," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-20, October.
    13. Bottasso, Anna & Conti, Maurizio & Ferrari, Claudio & Tei, Alessio, 2014. "Ports and regional development: A spatial analysis on a panel of European regions," Transportation Research Part A: Policy and Practice, Elsevier, vol. 65(C), pages 44-55.
    14. Wongsa-art, Pipat & Kim, Namhyun & Xia, Yingcun & Moscone, Francesco, 2024. "Varying coefficient panel data models and methods under correlated error components: Application to disparities in mental health services in England," Regional Science and Urban Economics, Elsevier, vol. 106(C).
    15. Brian Piper, 2014. "Factor-Specific Productivity," Working Papers 1401, Sam Houston State University, Department of Economics and International Business.
    16. repec:rre:publsh:v:40:y:2010:i:3:p:287-301 is not listed on IDEAS
    17. Mamkhezri, Jamal & Khezri, Mohsen, 2024. "Vehicle miles traveled induced demand, rebound effect, and price and income elasticities: A US spatial econometric analysis," Transport Policy, Elsevier, vol. 158(C), pages 224-240.
    18. Daza, Brian, 2024. "Government Spending Multipliers and Distribution of Commodity Booms in the Spatial Economy," Working Papers 690, Research Seminar in International Economics, University of Michigan.
    19. Juan Felipe Riaño & Felipe Valencia Caicedo, 2024. "Collateral Damage: The Legacy of the Secret War in Laos," The Economic Journal, Royal Economic Society, vol. 134(661), pages 2101-2140.
    20. Montmartin, Benjamin & Herrera, Marcos & Massard, Nadine, 2018. "The impact of the French policy mix on business R&D: How geography matters," Research Policy, Elsevier, vol. 47(10), pages 2010-2027.
    21. Yaoyao Wang & Yuanpei Kuang, 2023. "Evaluation, Regional Disparities and Driving Mechanisms of High-Quality Agricultural Development in China," Sustainability, MDPI, vol. 15(7), pages 1-20, April.
    22. Tizheng Li & Xiaojuan Kang, 2022. "Variable selection of higher-order partially linear spatial autoregressive model with a diverging number of parameters," Statistical Papers, Springer, vol. 63(1), pages 243-285, February.

    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:gam:jsusta:v:17:y:2025:i:8:p:3348-:d:1631209. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.