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Cited by 599

The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.

The role of green finance in reducing CO2 emissions: An empirical analysis

Open access
Saeed Meo, Muhammad, Karim, Mohd Zaini Abd·2022·Borsa Istanbul Review

Abstract

This study examines the relationship between green finance and carbon dioxide (CO2) emissions in the top ten economies that support green finance (Canada, Denmark, Hong Kong, Japan, New Zealand, Norway, Sweden, Switzerland, the United Kingdom, and the United States). This study uses quantile on quantile regression (QQR), introduced by Sim and Zhou (2015), to examine the dependence structure between different quantiles of green finance and CO2 emissions. Our overall findings confirm the negative impact of green finance on CO2 emissions; however, this relationship varies across the different quantiles of the two variables. This variation might be due to green finance market conditions (e.g., bearish or bullish) and country-specific market conditions. The findings in the study confirm that green finance is the best financial strategy for reducing CO2 emissions.

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Crossref · 10.1016/j.bir.2021.03.002

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Metadata

Authors
Saeed Meo, M., & Karim, M. Z. A.
Venue
Borsa Istanbul Review
Publisher
Elsevier BV
Published
2022-01
Volume
22
Issue
1
Pages
169-178
ISSN
2214-8450 (ISSN-L)
OpenAlex
W3137937089
Cited by
599
References
18
FWCI
73.33
Type
journal article
Language
en
Open access
gold

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CoChat. (2026). Citation check: The role of green finance in reducing CO2 emissions: An empirical analysis. https://app.cochat.ai/papers/10.1016/j.bir.2021.03.002
The paper
Saeed Meo, M., & Karim, M. Z. A. (2022). The role of green finance in reducing CO2 emissions: An empirical analysis. Borsa Istanbul Review. https://doi.org/10.1016/j.bir.2021.03.002

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.