Forecasting biocapacity and ecological footprint at a worldwide level to 2030 using neural networks

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dc.contributor.authorMoros Ochoa, Maria Andreína
dc.contributor.authorCastro Nieto, Gilmer Yovani
dc.contributor.authorQuintero, Anderson
dc.contributor.authorLlorente Portillo, Carolina
dc.date.accessioned2025-10-10T10:21:09Z
dc.date.available2025-10-10T10:21:09Z
dc.date.issued2022-08-27
dc.date.updated2025-10-10T10:21:09Z
dc.description.abstractConstant environmental deterioration is a problem widely addressed by multiple international organizations. However, given the current economic and technological limitations, alternatives that immediately and significantly impact environmental degradation negatively affect contemporary development and lifestyle. Because of this, rather than limiting population consumption patterns or developing sophisticated and highly expensive technologies, the solution to environmental degradation lies more in the progressive transformation of production and consumption patterns. Thus, to support this change, the objective of this article is to forecast the behavior of consumption and regeneration of biologically productive land until the year 2030, using a deep neural network adjusted to Global Footprint Network data for prediction, and to provide information that favors the development of local economic strategies based on the territorial strengths and weaknesses of each continent. The most relevant findings about biocapacity and ecological footprint data are: fishing grounds have the great renewable potential in the global consumption of products and focused on the Asian region being approximately 55% of the world’s ecological footprint; grazing lands indicate an exponential growth in terms of ecological footprint, however South America and Africa have almost 55% of the distribution in the world biocapacity, being great powers in the generation of agricultural products; forest lands show a decrease in biocapacity, there is a progressive and exponential deterioration of forest resources, the highest deficit in the world is generated in Asia; cropland presents an environmental balance between biocapacity and ecological footprint; and built land generates great impacts on development and regeneration in other lands, indicating the exponential crisis that could eventually be established by needing more and more resources from large built metropolises to replace the natural life provided by other lands.en
dc.description.sponsorshipColegio de Estudios Superiores de Administración by María Andreína Moros-Ochoa and the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No. 847624en
dc.identifier.citationMoros-Ochoa, M. A., Castro-Nieto, G. Y., Quintero-Español, A., & Llorente-Portillo, C. (2022). Forecasting biocapacity and ecological footprint at a worldwide level to 2030 using neural networks. Sustainability (Switzerland), 14(17). https://doi.org/10.3390/SU141710691
dc.identifier.doi10.3390/SU141710691
dc.identifier.eissn2071-1050
dc.identifier.urihttps://hdl.handle.net/20.500.14454/3933
dc.language.isoeng
dc.publisherMDPI
dc.rights© 2021 by Carolina Llorente-Portillo.
dc.subject.otherBiocapacity
dc.subject.otherEcological footprint
dc.subject.otherNeural networks
dc.subject.otherSustainable business models
dc.titleForecasting biocapacity and ecological footprint at a worldwide level to 2030 using neural networksen
dc.typejournal article
dcterms.accessRightsopen access
oaire.citation.issue17
oaire.citation.titleSustainability (Switzerland)
oaire.citation.volume14
oaire.licenseConditionhttps://creativecommons.org/licenses/by/4.0/
oaire.versionVoR
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