IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i7p2950-d1368909.html
   My bibliography  Save this article

Does New Urbanization Support the Rural Inclusive Green Development under Domestic Circulation in China?

Author

Listed:
  • Yuelei Hua

    (School of Finance and Economics, Jiangsu University, Zhenjiang 212013, China)

  • Jize Zhang

    (School of Humanities, Southwest Jiaotong University, Chengdu 611700, China)

  • Xuhui Ding

    (School of Finance and Economics, Jiangsu University, Zhenjiang 212013, China)

  • Guoping Ding

    (School of Finance and Economics, Jiangsu University, Zhenjiang 212013, China)

Abstract

New urbanization is an endogenous driving force to enhance domestic circulation. Driving the development of rural industries with urbanization to achieve interactive symbiosis has become an important topic to promote the coordinated development of urban and rural green. Based on the panel data of 30 provinces in China from 2009 to 2021, this paper constructs an evaluation index system for new urbanization and rural inclusive green development, and uses principal component analysis and panel regression model to analyze the impact of new-type urbanization on inclusive green development in rural areas. The results of the study show the following: (1) Rural inclusive green development and new urbanization have been significantly improved during the study period, but there are significant regional differences. (2) The construction of the new urbanization significantly promotes rural inclusive green development, but there is significant spatial heterogeneity. This effect is more significant in the Eastern and Central regions. (3) Population urbanization, land urbanization, social urbanization, and environmental urbanization can effectively promote rural inclusive green development, but economic urbanization will have a negative impact on green development in the countryside during the study period. Therefore, it is necessary to further strengthen the leading role of central cities and urban agglomerations, to promote the countryside with the city and at the same time to combat environmental pollution and to create ecologically livable towns and villages. In addition, the government should strengthen top-level design, provide industrial support to backward areas, improve the spatial layout of urbanization, and promote the deepening of new urbanization.

Suggested Citation

  • Yuelei Hua & Jize Zhang & Xuhui Ding & Guoping Ding, 2024. "Does New Urbanization Support the Rural Inclusive Green Development under Domestic Circulation in China?," Sustainability, MDPI, vol. 16(7), pages 1-19, April.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:7:p:2950-:d:1368909
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/7/2950/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/16/7/2950/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wu, Binrong & Wang, Lin & Zeng, Yu-Rong, 2022. "Interpretable wind speed prediction with multivariate time series and temporal fusion transformers," Energy, Elsevier, vol. 252(C).
    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. Katarzyna Rudnik & Anna Hnydiuk-Stefan & Aneta Kucińska-Landwójtowicz & Łukasz Mach, 2022. "Forecasting Day-Ahead Carbon Price by Modelling Its Determinants Using the PCA-Based Approach," Energies, MDPI, vol. 15(21), pages 1-23, October.
    2. Shengxiang Lv & Lin Wang & Sirui Wang, 2023. "A Hybrid Neural Network Model for Short-Term Wind Speed Forecasting," Energies, MDPI, vol. 16(4), pages 1-18, February.
    3. Wuyue An & Lin Wang & Dongfeng Zhang, 2023. "Comprehensive commodity price forecasting framework using text mining methods," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1865-1888, November.
    4. Nascimento, Erick Giovani Sperandio & de Melo, Talison A.C. & Moreira, Davidson M., 2023. "A transformer-based deep neural network with wavelet transform for forecasting wind speed and wind energy," Energy, Elsevier, vol. 278(C).
    5. Lv, Sheng-Xiang & Wang, Lin, 2023. "Multivariate wind speed forecasting based on multi-objective feature selection approach and hybrid deep learning model," Energy, Elsevier, vol. 263(PE).
    6. Karla Schröder & Gonzalo Farias & Sebastián Dormido-Canto & Ernesto Fabregas, 2024. "Comparative Analysis of Deep Learning Methods for Fault Avoidance and Predicting Demand in Electrical Distribution," Energies, MDPI, vol. 17(11), pages 1-13, June.
    7. Shengcai Zhang & Changsheng Zhu & Xiuting Guo, 2024. "Wind-Speed Multi-Step Forecasting Based on Variational Mode Decomposition, Temporal Convolutional Network, and Transformer Model," Energies, MDPI, vol. 17(9), pages 1-22, April.
    8. Zhang, Chu & Qiao, Xiujie & Zhang, Zhao & Wang, Yuhan & Fu, Yongyan & Nazir, Muhammad Shahzad & Peng, Tian, 2024. "Simultaneous forecasting of wind speed for multiple stations based on attribute-augmented spatiotemporal graph convolutional network and tree-structured parzen estimator," Energy, Elsevier, vol. 295(C).
    9. Du, Pei & Yang, Dongchuan & Li, Yanzhao & Wang, Jianzhou, 2024. "An innovative interpretable combined learning model for wind speed forecasting," Applied Energy, Elsevier, vol. 358(C).
    10. Fatma Mazen Ali Mazen & Yomna Shaker & Rania Ahmed Abul Seoud, 2023. "Forecasting of Solar Power Using GRU–Temporal Fusion Transformer Model and DILATE Loss Function," Energies, MDPI, vol. 16(24), pages 1-24, December.
    11. Ahmad Zia Ul-Saufie & Nurul Haziqah Hamzan & Zulaika Zahari & Wan Nur Shaziayani & Norazian Mohamad Noor & Mohd Remy Rozainy Mohd Arif Zainol & Andrei Victor Sandu & Gyorgy Deak & Petrica Vizureanu, 2022. "Improving Air Pollution Prediction Modelling Using Wrapper Feature Selection," Sustainability, MDPI, vol. 14(18), pages 1-16, September.
    12. Li, Yang & Shen, Xiaojun & Zhou, Chongcheng, 2023. "Dynamic multi-turbines spatiotemporal correlation model enabled digital twin technology for real-time wind speed prediction," Renewable Energy, Elsevier, vol. 203(C), pages 841-853.
    13. Ke Xu & Junli Zhang & Junhao Huang & Hongbo Tan & Xiuli Jing & Tianxiang Zheng, 2024. "Forecasting Visitor Arrivals at Tourist Attractions: A Time Series Framework with the N-BEATS for Sustainable Tourism," Sustainability, MDPI, vol. 16(18), pages 1-31, September.
    14. Wu, Binrong & Yu, Sihao & Peng, Lu & Wang, Lin, 2024. "Interpretable wind speed forecasting with meteorological feature exploring and two-stage decomposition," Energy, Elsevier, vol. 294(C).
    15. Athanasios Ioannis Arvanitidis & Dimitrios Bargiotas & Dimitrios Kontogiannis & Athanasios Fevgas & Miltiadis Alamaniotis, 2022. "Optimized Data-Driven Models for Short-Term Electricity Price Forecasting Based on Signal Decomposition and Clustering Techniques," Energies, MDPI, vol. 15(21), pages 1-24, October.
    16. Parri, Srihari & Teeparthi, Kiran & Kosana, Vishalteja, 2024. "A hybrid methodology using VMD and disentangled features for wind speed forecasting," Energy, Elsevier, vol. 288(C).
    17. Bentsen, Lars Ødegaard & Warakagoda, Narada Dilp & Stenbro, Roy & Engelstad, Paal, 2023. "Spatio-temporal wind speed forecasting using graph networks and novel Transformer architectures," Applied Energy, Elsevier, vol. 333(C).
    18. Konstantinos Blazakis & Yiannis Katsigiannis & Georgios Stavrakakis, 2022. "One-Day-Ahead Solar Irradiation and Windspeed Forecasting with Advanced Deep Learning Techniques," Energies, MDPI, vol. 15(12), pages 1-25, June.
    19. Jiafei Huan & Li Deng & Yue Zhu & Shangguang Jiang & Fei Qi, 2024. "Short-to-Medium-Term Wind Power Forecasting through Enhanced Transformer and Improved EMD Integration," Energies, MDPI, vol. 17(10), pages 1-22, May.
    20. Liu, Chenyu & Zhang, Xuemin & Mei, Shengwei & Zhou, Qingyu & Fan, Hang, 2023. "Series-wise attention network for wind power forecasting considering temporal lag of numerical weather prediction," Applied Energy, Elsevier, vol. 336(C).

    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:16:y:2024:i:7:p:2950-:d:1368909. 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.