未充分利用土地与可持续发展：对就业、经济产出及CO2排放减缓的影响
Underutilized land and sustainable development: effects on employment, economic output, and mitigation of CO2 emissions
AI总结：
本文结合AI与遥感技术，利用YOLOv8和检索增强生成方法优化阿塞拜疆加巴拉区的植树造林策略，并通过时间序列模型预测其对减少CO2排放的积极影响。
摘要：
气候变化、森林砍伐和生物多样性丧失正呼唤创新方法以实现有效的重新造林和植树造林。本文探讨了在阿塞拜疆加巴拉区整合人工智能与遥感技术，以优化树木种植策略、估算劳动力需求并确定各种树种的空间需求。研究采用YOLOv8精确识别潜在种植地点，并采用结合Gemini API的检索增强生成方法提供定制化物种推荐。该方法结合时间序列建模预测重新造林对减少CO2排放的影响，利用Holt-Winters进行预测。结果表明，AI模型能有效识别合适的地点和物种，为大规模植树造林潜在的经济与环境效益提供了宝贵见解，从而促进经济可持续发展并帮助缓解全球变暖和气候变化的不利影响。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
