Green Financing, Leverage, and Dividend Policy Dynamics: Evidence From a Causal Machine Learning and Bayesian Framework

Citation

Gyamfi, Bright Akwasi and Yadav, Ashutosh and Arhinful, Richard and Khudoykulov, Khurshid (2026) Green Financing, Leverage, and Dividend Policy Dynamics: Evidence From a Causal Machine Learning and Bayesian Framework. Business Strategy and the Environment. ISSN 0964-4733

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Green Financing, Leverage, and Dividend Policy Dynamics_ Evidence From a Causal Machine Learning and Bayesian Framework - Gyamfi - Business Strategy and the Environment - Wiley Online Library.pdf - Published Version
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Abstract

This study examines how sustainable finance shapes dividend policy among 143 nonfinancial firms listed on the London Stock Exchange—a market with strict sustainability disclosure standards—over 2007–2023, using firm-level data from Thomson Reuters Eikon DataStream. To capture average, heterogeneous, and time-varying effects, it combines double machine learning, causal forest estimation, quantile treatment effects, and Bayesian time-varying models. Green bond issuance is associated with lower dividend payouts, consistent with firms preserving liquidity for long-term environmental investment, whereas improvements in emission efficiency and environmental policy performance are associated with higher dividend ratios, indicating that markets reward verifiable environmental performance. Leverage moderates these relationships, with payout responses strengthening among more indebted firms. The study provides new evidence that sustainable finance influences corporate payouts through two distinct channels—financial commitment and performance signaling—offering insights for managers, policymakers, and scholars in sustainable corporate finance.

Item Type: Article
Uncontrolled Keywords: Green bond issuance, sustainable finance
Subjects: H Social Sciences > HG Finance
Divisions: Faculty of Management (FOM)
Depositing User: Ms Rosnani Abd Wahab
Date Deposited: 04 Sep 2026 02:34
Last Modified: 04 Sep 2026 02:34
URII: http://shdl.mmu.edu.my/id/eprint/16685

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