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Cited by 561

The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.

Loaning scale and government subsidy for promoting green innovation

Huang, Zhehao, Liao, Gaoke, Li, Zhenghui·2019·Technological Forecasting and Social Change

Abstract

Abstract Green innovation has attracted worldwide attention in the past decades. In this paper, we develop series of game models to address the effects of green loans and government subsidies on green innovation activities of enterprises. We derive a threshold value for loaning interest rate. If the interest rate of the green loan is lower than this threshold value, then the enterprises are willing to accept the loan from the bank and implement green innovation. By defining a measure for environmental effect of productive activities, we obtain a threshold value for the loaning scale. If the loaning scale is larger than this threshold value, then it meets the purpose of green loan to improve the environmental quality. We prove the effectiveness of government subsidies as an intervention way in supporting green innovation and environmental protection.

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Crossref · 10.1016/j.techfore.2019.04.023

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Metadata

Authors
Huang, Z., Liao, G., & Li, Z.
Venue
Technological Forecasting and Social Change
Publisher
Elsevier BV
Published
2019-07
Volume
144
Pages
148-156
ISSN
0040-1625 (ISSN-L)
OpenAlex
W2942028808
Cited by
561
References
32
FWCI
59.58
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Loaning scale and government subsidy for promoting green innovation. https://app.cochat.ai/papers/10.1016/j.techfore.2019.04.023
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Huang, Z., Liao, G., & Li, Z. (2019). Loaning scale and government subsidy for promoting green innovation. Technological Forecasting and Social Change. https://doi.org/10.1016/j.techfore.2019.04.023

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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.