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  <doi_batch_id>aspg-38-2419-1792223542</doi_batch_id>
  <timestamp>20261017075222</timestamp>
  <depositor>
   <depositor_name>American Scientific Publishing Group</depositor_name>
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  <journal>
   <journal_metadata language="en">
    <full_title>Journal of Sustainable Development and Green Technology</full_title>
    <abbrev_title>JSDGT</abbrev_title>
    <issn media_type="electronic">2836-5399</issn>
   </journal_metadata>
   <journal_issue>
    <publication_date media_type="online">
     <year>2024</year>
    </publication_date>
    <journal_volume>
     <volume>4</volume>
    </journal_volume>
    <issue>1</issue>
   </journal_issue>
   <journal_article publication_type="full_text">
    <titles>
     <title>Leveraging Business Intelligence for Enhanced Green Financial Practices in Advanced Corporations</title>
    </titles>
    <contributors>
     <person_name sequence="first" contributor_role="author">
      <given_name>Mohamed</given_name>
      <surname>Eassa</surname>
      <affiliations>
       <institution>
        <institution_name>Computer science depatment, faculty of information systems and computer science, october 6 university, Eygpt</institution_name>
       </institution>
      </affiliations>
     </person_name>
     <person_name sequence="additional" contributor_role="author">
      <given_name>Nariman A.</given_name>
      <surname>Khaliel</surname>
      <affiliations>
       <institution>
        <institution_name>Assistant professor in ECCAT Suez university, Eygpt</institution_name>
       </institution>
      </affiliations>
     </person_name>
    </contributors>
    <jats:abstract>
     <jats:p>The research aims to fill a gap in the current sustainability strategies by investigating how business intelligence can be integrated into advanced companies to improve green financial practices. We will apply our proposed framework to Mutual Funds and Exchange-Traded Funds (ETFs) as we recognize the need for environmentally responsible financial decisions. Our study uses statistical analysis and predictive modelling with Random Forest and Ordinary Least Squares based on a comprehensive dataset obtained from Yahoo Finance. The results, presented through sector distributions, risk ratings, and distribution by category, provide detailed insights into the multifaceted impacts of business intelligence. Our findings indicate that the suggested framework optimises financial decisions and empha-sizes the importance of customized approaches across different financial instruments. This study provides a valuable roadmap for practitioners, policymakers, and researchers navigating the changing landscape of environmentally responsible financial strategies in an era where advanced corporations grapple with the complexities of sustainable finance.</jats:p>
    </jats:abstract>
    <publication_date media_type="online">
     <year>2024</year>
    </publication_date>
    <pages>
     <first_page>07</first_page>
     <last_page>11</last_page>
    </pages>
    <publisher_item>
     <item_number item_number_type="article-number">2419</item_number>
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     <ai:license_ref applies_to="vor">https://creativecommons.org/licenses/by/4.0/</ai:license_ref>
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     <doi>10.54216/JSDGT.040102</doi>
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