Abstract
Purpose: Environmental, social and governance factors have gained significant traction for investors to evaluate their investment decisions and achieve higher impact investments. Societies have had a growing impact on investors to ensure that besides profitability factors, the impact on the environment, society and internal governance criteria are taken into account when allocating funds. This has led to a growing divestment from highly polluting industries and corporations that are not diverse in their workforce, or have a negative impact on the society such as using forced labor. Islamic finance encompasses the principles of Shariah law that put a strong focus on preserving the environment and support the society. Evaluating whether corporations are Shariah compliant with respect to the environment is challenging, as environmental ESG scores may not adequately represent the entire impact on the environment and the Shariah-environment compliance of corporations. Method: This article presents a new data-driven framework for the assessment of Shariah – environmental compliance for corporations in addition to their financial performance. Results and conclusion: An analysis based on large Islamic compliant US listed enterprises indicate strong clustering performance, and differentiation in terms of Islamic environmental compliance versus non-compliance. Originality: The deep learning framework incorporates an unsupervised-random forest learning approach at categorizing environmental compliance while simultaneously estimating the financial performance of these corporations.
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