مجلة المنتدى للدراسات والابحاث الاقتصادية
Volume 9, Numéro 2, Pages 428-445
2025-12-07
Harnessing Artificial Intelligence For Foresight In The U.s. Circular Carbon Economy Transition: A Quantitative Economic Perspective
Authors : Ben Ammar Dawed . Mahri Abdelmalek .
Abstract
This study aims to integrate deep learning (DL) with environmentally extended input-output (EEIO) analysis to model the U.S. circular carbon economy (CCE). The objective is to forecast key CCE indicators, such as carbon capture rates and emissions reduction pathways, by leveraging AI’s pattern recognition and economic modeling’s structural clarity. Results demonstrate that the hybrid framework provides data-rich insights into carbon flows and economic impacts, as shown through illustrative tables and figures. Despite challenges in data sourcing and model integration, the approach offers actionable guidance for policymakers and industry leaders to navigate the CCE transition.
Keywords
Circular Carbon Economy ; Artificial Intelligence ; Quantitative Economics ; Deep Learning ; Input–Output Analysis ; Carbon Capture Utilization and Storage ; Climate Policy ; United States ; Sustainable Development
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