Underutilized land and sustainable development: effects on employment, economic output, and mitigation of CO2 emissions

TL;DR

This paper demonstrates how integrating AI and remote sensing can optimize reforestation efforts, enhance employment, boost economic output, and reduce CO2 emissions, contributing to sustainable development.

Contribution

It introduces a novel AI-based framework combining YOLOv8 and Retrieval-Augmented Generation for site selection and species recommendation in reforestation projects.

Findings

AI effectively identifies suitable planting sites and species

Reforestation can significantly reduce CO2 emissions

Potential economic benefits from large-scale tree planting

Abstract

Climate change, deforestation, and biodiversity loss are calling for innovative approaches to effective reforestation and afforestation. This paper explores the integration of artificial intelligence and remote sensing technologies for optimizing tree planting strategies, estimating labor requirements, and determining space needs for various tree species in Gabala District of Azerbaijan. The study employs YOLOv8 for precise identification of potential planting sites and a Retrieval-Augmented Generation approach, combined with the Gemini API, to provide tailored species recommendations. The methodology incorporates time-series modeling to forecast the impact of reforestation on CO2 emissions reduction, utilizing Holt-Winters for predictions. Our results indicate that the AI model can effectively identify suitable locations and species, offering valuable insights into the potential economic and environmental benefits of large-scale tree planting thus fostering sustainable economic development and helping to mitigate the adverse effects of global warming and climate change.