IDEAS home Printed from https://ideas.repec.org/a/hin/jnlmpe/5191929.html
   My bibliography  Save this article

Comprehensive Accounting of Resources, Environment, and Economy Integrating Machine Learning and Establishment of Green GDP

Author

Listed:
  • Baoyu Chen
  • Feng Li
  • Sagheer Abbas

Abstract

In macroeconomics, GDP is one of the most important concepts and indicators. GDP has always been the most common indicator of national economic development in the world. With the development of the environmental protection movement and the rise of the sustainable concept, it is urgent to carry out resource and environmental accounting to comprehensively reflect the resource and environmental input in economic development. Green GDP is gradually developed in this context. The article introduces machine learning technology and data mining technology to establish a comprehensive accounting system for resource and environmental economy and green GDP. The data related to a city’s green GDP is collected by mining and the environmental ecological cost is constructed to correct the city to obtain its green GDP value. Taking a city as an example and confirming the method proposed in this article, the final GDP of the city is 68.839 billion yuan, the environmental ecological cost is 4.635 billion yuan, and the green GDP is 64.204 billion yuan.

Suggested Citation

  • Baoyu Chen & Feng Li & Sagheer Abbas, 2022. "Comprehensive Accounting of Resources, Environment, and Economy Integrating Machine Learning and Establishment of Green GDP," Mathematical Problems in Engineering, Hindawi, vol. 2022, pages 1-11, June.
    • Handle: RePEc:hin:jnlmpe:5191929
    DOI: 10.1155/2022/5191929
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/mpe/2022/5191929.pdf
    Download Restriction: no
    File URL: http://downloads.hindawi.com/journals/mpe/2022/5191929.xml
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2022/5191929?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
    ---><---

    More about this item

    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:hin:jnlmpe:5191929. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.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.