Providing and analysis of green financing model of companies through the banking industry in establishing a sustainable environment

Subject Areas : Financial Economics

عباسعلی شیخ ¹ , پرویز سعیدی ² , ابراهیم عباسی ³ , آرش نادریان ⁴

1 - گروه مهندسی مالی، واحد علی آباد کتول، دانشگاه آزاد اسلامی ، علی آباد کتول ، ایران.
2 - گروه حسابداری و مالی، واحد علی آباد کتول ، دانشگاه آزاد اسلامی ، علی آباد کتول ، ایران(
3 - گروه اقتصاد،دانشگاه الزهرا (س)، تهران ، ایران.
4 - گروه حسابداری دانشگاه آزاد اسلامی واحد علی آباد کتول ، ایران

Received: 2021-12-23 Accepted : 2022-02-24 Published : 2022-04-21

Keywords: Banking industry, Interpretive Structural Modeling, Keywords: Green Financing, Structural Equations Technique, Grounded Theory JEL Classificatio: Q14.Q32.P28.G2,

Abstract :

Abstract Environmental protection and sustainable environmental development are important concerns of human societies and the main unquestionable. Banks, considering the environmental risks in financing companies, play an essential role in protecting the environment and moving towards a green economy. As one of the dimensions of green banking, green financing is an innovative financial instrument that tries to improve the environmental balance with the industries' development and economic growth. The present research aimed to design and analyze the financing model of companies through the Iranian banking industry in establishing a sustainable environment, which has a three-step approach. First, by interviewing 16 experts and encode, 77 components in the form of 21 categories were identified based on Grounded data theory, and the model related to design and hypotheses were identified. And next, the structural equation modeling method was used to evaluate the model-fitting and hypotheses test, which showed the significant results of the measuring and structural models, the optimal model fitting, and hypotheses confirmation. Then, by using the interpretive structural modeling method, the components of green financing were prioritized, in this stage, 20 components with the highest frequency of code were entered, and finally, the priority of the components was recognized in 6 levels.

References:

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