Abstract:

In order to analyze their investment choices and achieve better impact investments, investors are increasingly considering environmental, social, and Governance aspects. Investors are under increasing pressure from society to make sure that, in addition to profitability reasons, the environment's effect, society's impact, and corporate governance standards are taken into consideration when allocating funds. As a result, there has been an increase in the divestment of firms that use forced labor, lack diversity in their workforces, and operate in highly polluting sectors. Islamic banking incorporates Shariah law's guiding principles, which place a heavy emphasis on protecting the environment and advancing society. It can be difficult to determine if firms are Shariah-compliant in terms of the environment since environmental ESG ratings could not accurately reflect all of a corporation's environmental effects or its compliance with Shariah. In addition to evaluating a company's financial success, this article introduces a new data-driven approach for assessing its Shariah-compliant environmental performance. The deep learning system uses an unsupervised-random forest learning method to classify environmental compliance while also measuring these firms' financial performance. Large Islamic-compliant US listed firms were the subject of an investigation, which revealed high clustering performance and a difference between Islamic environmental compliance and non-compliance.

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