JOURNAL ARTICLE

Testing the Economic Growth Path "Green-Resilience" Under Natural Resources Constraint in Asia-Pacific Economies.

  • Published In: Journal of Environmental Assessment Policy & Management, 2023, v. 25, n. 2. P. 1 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Tchouto, Jules-Eric Tchapchet 3 of 3

Abstract

This study investigates the existence of the environmental Kuznets curve hypothesis under natural resources (NRs) constraint in nine Asian-Pacific countries with data spanning from 2000 to 2019. Using Pooled OLS estimations, results show that economic growth is on a pathway from which environmental quality is improved. Hypothesising that NRs exploitation is a potential factor that can exacerbate environmental quality, the study shows that the Asian economic structure is on a "green-resilience" path when controlling for total and each NRs component. The magnitude of each NR component in the ability of increasing CO2 emissions is characterised. Results are robust with different methods of estimations (control variables, alternative dependent variable, Two Stages Least Squares (2SLS)-3SLS Instrumental Variables strategies, Panel-Corrected Standard Error (PCSE) and Driscoll and Kraay methods). As policy implications, Asia-Pacific countries should amplify eco-innovation, development of renewable energies and fiscal policies as they positively impact FDI on green growth. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Journal of Environmental Assessment Policy & Management. 2023/06, Vol. 25, Issue 2, p1
  • Document Type:Article
  • Subject Area:Economics
  • Publication Date:2023
  • ISSN:1464-3332
  • DOI:10.1142/S1464333223500102
  • Accession Number:164766874
  • Copyright Statement:Copyright of Journal of Environmental Assessment Policy & Management is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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