JOURNAL ARTICLE

Exploring Greenium and the Determinants of Green Bond Performance in Asia.

  • Published In: Journal of Environmental Assessment Policy & Management, 2024, v. 26, n. 4. P. 1 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Nurvita, Tita; Achsani, Noer Azam; Anggraeni, Lukytawati; Malahayati, Marissa; Novianti, Tanti 3 of 3

Abstract

This study examines the presence and determinants of the 'greenium' in Asian green bonds. The dataset includes green bonds issued by governments and corporations in Asian countries from 2016 to 2022. To analyse the determinants of the greenium, we applied a coarsened exact matching (CEM) model, an advanced form of the matching method. Ordinary least squares (OLS) regression was employed to assess the factors influencing the greenium. Our findings indicate that a greenium exists in green bonds issued in Asia, with significant effects observed in China, Korea, and Japan. Key drivers of the greenium include coupon rates, issuer type, oil prices, and the federal funds rate. This study contributes practical insights by highlighting that a greenium can be achieved through strategic project selection, transparent communication, and strong environmental commitment. Additionally, investors should account for coupon rates, issuer sector, and macroeconomic factors such as oil prices and the federal funds rate when investing in green bonds. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Journal of Environmental Assessment Policy & Management. 2024/12, Vol. 26, Issue 4, p1
  • Document Type:Article
  • Subject Area:Business and Management
  • Publication Date:2024
  • ISSN:1464-3332
  • DOI:10.1142/S1464333224500157
  • Accession Number:181949725
  • 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.)

Looking to go deeper into this topic? Look for more articles on EBSCOhost.