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Artificial intelligence in biocapacity and ecological footprint prediction in latin America and the caribbean

dc.contributor.authorvan der Woude, David
dc.contributor.authorCastro Nieto, Gilmer Yovani
dc.contributor.authorMoros Ochoa, Maria Andreina
dc.contributor.authorLlorente Portillo, Carolina
dc.contributor.authorQuintero Español, Anderson
dc.date.accessioned2025-02-25T20:43:32Z
dc.date.available2025-02-25T20:43:32Z
dc.date.issued2024-06-14
dc.identifier.issn1387-585X
dc.identifier.urihttp://hdl.handle.net/10726/5787
dc.language.isoeng
dc.publisherSpringer
dc.titleArtificial intelligence in biocapacity and ecological footprint prediction in latin America and the caribbeaneng
dc.typearticle
dc.rights.accessrightsinfo:eu-repo/semantics/restrictedAccess
dc.identifier.localArt014
dc.rights.localAcceso Restringido
dc.type.versioninfo:eu-repo/semantics/acceptedVersion
dc.identifier.instnameinstname:Colegio de Estudios Superiores de Administración – CESA
dc.identifier.reponamereponame:Biblioteca Digital – CESA
dc.identifier.repourlrepourl:https://repository.cesa.edu.co/
dc.description.abstractenglishIn the face of decades of unsustainable development that has led to significant depletion of resources and environmental imbalances, the need for advanced methods to understand and mitigate adverse environmental effects has never been more critical. This study introduces an innovative approach using Artificial Neural Networks (ANN) to predict the biocapacity and ecological footprint, focusing on the forest land indicator in Latin America and the Caribbean up to 2030, aligning with the Sustainable Development Goals (SDGs). Utilizing the Python programming language and leveraging the TensorFlow library for its robustness in handling complex datasets, we designed a neural network model that underwent thirty thousand iterations to identify the optimal processing time, approximately five minutes per dataset. Our analysis includes 57 annual records across 128 countries, highlighting the region’s rich natural resources. The findings underscore the critical importance of developing sustainable business models that responsibly harness these resources, offering stakeholders fresh opportunities to engage in sustainable development practices actively. Moreover, the study serves as a vital roadmap for other developing regions aspiring to enhance their environmental sustainability strategies and climate change mitigation efforts. By accurately predicting biocapacity and ecological footprints, this research not only aids in the strategic planning of sustainable development but also sets a precedent for applying artificial intelligence in environmental science, offering a novel approach for policymakers and business practitioners alike in Latin America and the Caribbean. These findings provide a practical guide for policymakers and business practitioners to develop sustainable business models and enhance environmental sustainability strategies.eng
dc.type.coarhttp://purl.org/coar/resource_type/c_2df8fbb1
dc.contributor.orcidvan der Woude, David [0000-0003-1682-9481]
dc.contributor.orcidCastro Nieto, Gilmer Yovani [0000-0001-9861-5588]
dc.contributor.orcidMoros Ochoa, María Andreína [0000-0001-8428-9056]
dc.contributor.orcidLlorente Portillo, Carolina [0000-0002-2350-5891]
dc.contributor.orcidQuintero Español, Anderson [0000-0002-6562-6245]
dc.type.driverinfo:eu-repo/semantics/article
dc.type.redcolhttp://purl.org/redcol/resource_type/ART
dc.type.coarversionhttp://purl.org/coar/version/c_71e4c1898caa6e32
dc.contributor.scopusvan der Woude, David [57204114134]
dc.contributor.scopusCastro Nieto, Gilmer Yovani [24544764500]
dc.contributor.scopusMoros Ochoa, María Andreína [57195503017]
dc.contributor.scopusLlorente Portillo, Carolina [57888736900]
dc.contributor.scopusQuintero Español, Anderson [57888736800]
dc.description.orcidhttps://orcid.org/0000-0003-1682-9481
dc.description.orcidhttps://orcid.org/0000-0001-9861-5588
dc.description.orcidhttps://orcid.org/0000-0001-8428-9056
dc.description.orcidhttps://orcid.org/0000-0002-2350-5891
dc.description.orcidhttps://orcid.org/0000-0002-6562-6245
dc.description.scopushttps://www.scopus.com/authid/detail.uri?authorId=57204114134
dc.description.scopushttps://www.scopus.com/authid/detail.uri?authorId=24544764500
dc.description.scopushttps://www.scopus.com/authid/detail.uri?authorId=57195503017
dc.description.scopushttps://www.scopus.com/authid/detail.uri?authorId=57888736900
dc.description.scopushttps://www.scopus.com/authid/detail.uri?authorId=57888736800
dc.identifier.eissn1573-2975
dc.relation.ispartofjournalEnvironment, Development and Sustainability
dc.identifier.urlhttps://link-springer-com.cvirtual.cesa.edu.co/article/10.1007/s10668-024-05101-7
dc.subject.proposalArtificial Neural Networks (ANN)
dc.subject.proposalBiocapacity
dc.subject.proposalEcological footprint
dc.subject.proposalForest land
dc.subject.proposalSustainable development


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