Artificial intelligence in biocapacity and ecological footprint prediction in Latin America and the Caribbean

David van der Woude (), Gilmer Yovani Castro Nieto, Maria Andreina Moros Ochoa, Carolina Llorente Portillo and Anderson Quintero
Additional contact information
David van der Woude: CESA School of Business
Gilmer Yovani Castro Nieto: Pontificia Universidad Javeriana
Maria Andreina Moros Ochoa: CESA School of Business
Carolina Llorente Portillo: University of Duesto
Anderson Quintero: CESA School of Business

Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, 2025, vol. 27, issue 9, No 101, 22925-22946

Abstract: Abstract In 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.

Keywords: Artificial Neural Networks (ANN); Biocapacity; Ecological footprint; Forest land; Sustainable development (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10668-024-05101-7 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:endesu:v:27:y:2025:i:9:d:10.1007_s10668-024-05101-7

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/10668

DOI: 10.1007/s10668-024-05101-7

Access Statistics for this article

Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development is currently edited by Luc Hens

More articles in Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().