IDEAS home Printed from https://ideas.repec.org/h/spr/prbchp/978-981-16-7818-9_4.html
   My bibliography  Save this book chapter

World Income Classification and Emissions: A Study of the Relationship Using Machine Learning Techniques

In: Achieving $5 Trillion Economy of India

Author

Listed:
  • Karthikeyan Chandramohan

    (National Institute of Technology)

  • Ramasamy Murugesan

    (National Institute of Technology)

Abstract

Using classification and clustering on cross-sectional data for 185 countries, this study explores if the level of emissions in a country is a determinant of the income class of a country. This study also tests the presence of EKC relationship between emissions and income across the countries. Income classification and emission data of countries obtained from World Bank has been used. The results, which can have a variety of policy implications, reveal: (1) clustering based on CO2 emissions alone has a considerable overlap with income classification, (2) CO2 emission per capita alone predicts the income class of a country and (3) presence of EKC relationship between CO2 emissions and income.

Suggested Citation

  • Karthikeyan Chandramohan & Ramasamy Murugesan, 2022. "World Income Classification and Emissions: A Study of the Relationship Using Machine Learning Techniques," Springer Proceedings in Business and Economics, in: Arti Chandani & Rajiv Divekar & J. K. Nayak (ed.), Achieving $5 Trillion Economy of India, pages 63-79, Springer.
    • Handle: RePEc:spr:prbchp:978-981-16-7818-9_4
    DOI: 10.1007/978-981-16-7818-9_4
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    More about this item

    Keywords

    Emissions; Income classification; EKC; Logit; Probit; Clustering; Regression;
    All these keywords.

    JEL classification:

    • O13 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Agriculture; Natural Resources; Environment; Other Primary Products
    • Q56 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Environment and Development; Environment and Trade; Sustainability; Environmental Accounts and Accounting; Environmental Equity; Population Growth

    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:spr:prbchp:978-981-16-7818-9_4. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.