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Harnessing Artificial Intelligence for Circular Waste Systems and Climate Resilience | ||
| مجله پژوهش های خشکسالی و تغییراقلیم | ||
| مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 23 فروردین 1405 اصل مقاله (671.05 K) | ||
| نوع مقاله: مقاله مروری | ||
| شناسه دیجیتال (DOI): 10.22077/jdcr.2026.10862.1208 | ||
| نویسندگان | ||
| محمد حسین صیادی* 1؛ Zahra Simaei2؛ Mohsen Nowrouzi3 | ||
| 1پژوهشگاه اقیانوس شناسی و علوم جوی | ||
| 2Bachelor student, Department of Natural Resources and Environment, Faculty of Agriculture, Shahid Bahonar University of Kerman, Kerman, Iran | ||
| 3Department of Natural Resources and Environmental Engineering, School of Agriculture, Shiraz University, Shiraz 71441-13131, Iran | ||
| چکیده | ||
| Climate change is widely recognized as one of the most critical global challenges, directly influenced by human activities, particularly the increasing generation and improper management of solid waste. The growing volume of municipal and industrial waste, especially in developing countries, contributes significantly to the emission of greenhouse gases such as carbon dioxide and methane, thereby threatening environmental sustainability. This issue not only exacerbates pollution across air, soil, and water resources but also intensifies public health risks and accelerates climate change impacts. Consequently, effective and sustainable waste management has emerged as a key strategy for mitigating greenhouse gas emissions. This study explores the role of artificial intelligence (AI) in enhancing waste management systems and reducing the environmental and climatic impacts associated with waste generation. Intelligent algorithms have the capability to predict waste generation trends, optimize transportation routes, automate material sorting, and evaluate climate-related policies, all of which contribute to reducing emissions and energy consumption. AI systems have achieved prediction accuracies of up to 98.5% in specific applications, such as monitoring bin fill levels. So, machine learning based predictive models can improve the timing and efficiency of waste collection, thereby decreasing fuel use and pollutant release. The findings indicate that integrating advanced AI technologies with environmental management approaches offers a robust pathway for addressing climate challenges. Such integration can significantly support sustainable development goals by lowering the carbon footprint, improving resource efficiency, and strengthening adaptive capacity to climate change. | ||
| کلیدواژهها | ||
| Global warming؛ Green technology؛ Greenhouse gas mitigation؛ Intelligent algorithms؛ Sustainable development | ||
| مراجع | ||
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آمار تعداد مشاهده مقاله: 332 تعداد دریافت فایل اصل مقاله: 49 |
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