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

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.