EconPapers    
Economics at your fingertips  
 

An Empirical Analysis Using Machine Learning to Identify Features Influencing the Green Economy of India

Boris Raj Borgohain and Krishna Kumar Singh

Jindal Journal of Business Research, 2022, vol. 11, issue 1, 7-23

Abstract: Due to the vast changes being experienced in the current world climate, sustainable development and green economy have become key terms as counters to the experienced climate changes. As such, this article analyzes the features of green economy that would be key in the world’s development towards a green economy and a sustainable environment. The article uses the conceptualization of green GDP as an indicator of green economic development for the analysis. The article uses dimension reduction techniques and machine learning algorithms to identify the features with high importance in green economy. Results of the analysis show that the defined factor technological investment has the highest importance on development of green economy, and economic growth also exhibits a considerable amount of importance. The study provides suggestions on the direction of policy development by stakeholders for increasing the green GDP, which acts as the indicator of green economic development.

Keywords: Green economy; sustainable development; green GDP; dimension reduction; machine learning (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://journals.sagepub.com/doi/10.1177/22786821221082611 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:sae:jjlobr:v:11:y:2022:i:1:p:7-23

DOI: 10.1177/22786821221082611

Access Statistics for this article

More articles in Jindal Journal of Business Research
Bibliographic data for series maintained by SAGE Publications ().

 
Page updated 2025-03-19
Handle: RePEc:sae:jjlobr:v:11:y:2022:i:1:p:7-23