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        <full_title>International Journal of Applied Sciences &amp; Development</full_title>
        <issn media_type="electronic">2945-0454</issn>
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        <titles>
          <title>A Neural Lotka–Volterra Approach to Modeling Sustainability Risks in Finance</title>
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        <contributors>
          <person_name sequence="first" contributor_role="author">
            <given_name>Manana</given_name>
            <surname>Chumburidze</surname>
            <affiliations>
              <institution>
                <institution_name>Department of Computer Technology Akaki Tsereteli State University #59 Tamar Mephe Street, Kutaisi GEORGIA</institution_name>
              </institution>
            </affiliations>
          </person_name>
          <person_name sequence="additional" contributor_role="author">
            <given_name>Giorgi</given_name>
            <surname>Chachua</surname>
            <affiliations>
              <institution>
                <institution_name>Department of Computer Technology Akaki Tsereteli State University #59 Tamar Mephe Street, Kutaisi GEORGIA</institution_name>
              </institution>
            </affiliations>
          </person_name>
          <person_name sequence="additional" contributor_role="author">
            <given_name>Teimuraz</given_name>
            <surname>Sakhelashvili</surname>
            <affiliations>
              <institution>
                <institution_name>Department of Computer Technology Akaki Tsereteli State University #59 Tamar Mephe Street, Kutaisi GEORGIA</institution_name>
              </institution>
            </affiliations>
          </person_name>
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        <jats:abstract>
          <jats:p>This article explores the challenges of sustainable finance and innovative risk management approaches in Georgia, employing a neural differential equation based on the Lotka–Volterra perspective (NLV) to model the complex interactions among financial institutions, environmental factors, and regulatory bodies. By applying this model, we examine how various risk management strategies—such as green bonds, carbon pricing, and environmental stress testing—can affect the stability and sustainability of Georgia's financial ecosystem. The study concludes with policy recommendations for the National Bank of Georgia and other stakeholders to foster a resilient and sustainable financial sector. To emphasize the interdisciplinary nature of the research, the article explicitly integrates insights from finance, environmental science, and mathematical modeling.</jats:p>
        </jats:abstract>
        <publication_date media_type="print">
          <month>07</month>
          <day>20</day>
          <year>2026</year>
        </publication_date>
        <publication_date media_type="online">
          <month>07</month>
          <day>20</day>
          <year>2026</year>
        </publication_date>
        <pages>
          <first_page>104</first_page>
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        <publisher_item>
          <item_number item_number_type="article_number">12</item_number>
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          <doi>10.37394/232029.2026.5.12</doi>
          <resource>https://wseas.com/journals/asd/2026/a24asd-012(2026).pdf</resource>
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        <citation_list>
          <citation key="ref0">
            <unstructured_citation>Mira-Cristiana Anisiu, Lotka, Volterra and their model Title of the Paper, DIDACTICA MATHEMATICA, Vol. 32(2014), pp. 9–17</unstructured_citation>
          </citation>
          <citation key="ref1">
            <unstructured_citation>Chumburidze, M., &amp; Niminet, V. (2025). Efficient Modelisation for Complex Geometries of Tumor Growth via ThermoElastic Diffusion Partial Differential Equations and Artificial Neural Networks. BRAIN. Broad Research in Artificial Intelligence and Neuroscience, 16(1), 315-323.</unstructured_citation>
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          <citation key="ref2">
            <unstructured_citation>Goswami, S., Bora, A., Yu, Y. and Karniadakis, G.E. (2023) Physics-informed Deep Neural Operator Networks. In: Rabczuk, T. and Bathe, K.J., Eds., Computational Methods in Engineering &amp; the Sciences, Springer International Publishing, 219-254.</unstructured_citation>
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          <citation key="ref3">
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          </citation>
          <citation key="ref4">
            <unstructured_citation>Hou, Xiaoran, et al. "Energy sustainability evaluation of 30 provinces in China using the improved entropy weight-cloud model." Ecological Indicators 126 (2021): 107657</unstructured_citation>
          </citation>
          <citation key="ref5">
            <unstructured_citation>Ioannou, I., &amp; Serafeim, G. The Consequences of Mandatory Corporate Sustainability Reporting. Harvard BusinessReview,95(1), 100-107(2017)</unstructured_citation>
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