Economic Impacts of Carbon Taxation Modeled Through Deep Learning Techniques

Abstract

The urgency of addressing climate change has led to the implementation of various policy measures, with carbon taxation emerging as a significant tool for reducing greenhouse gas emissions. This paper examines the economic impacts of carbon taxation through advanced deep learning techniques, highlighting the effectiveness of these models in capturing the complex, non-linear relationships between carbon emissions and various economic indicators. The study reveals that carbon taxation has both short-term and long-term economic implications. In the short term, industries reliant on fossil fuels may face increased operational costs and potential job losses, while the renewable energy sector experiences growth as employment opportunities shift. Over the long term, carbon taxation fosters structural changes within the economy, promoting innovation and investment in sustainable technologies. Furthermore, the environmental benefits associated with reduced carbon emissions can lead to significant cost savings in healthcare and environmental remediation, indicating that the advantages of carbon taxation extend beyond economic metrics alone. The study also identifies gaps in the current literature and suggests that future research should refine models to analyze the economic impacts of carbon taxation more effectively. This includes addressing data limitations and exploring the potential biases in existing datasets. Moreover, the integration of artificial intelligence in economic modeling presents opportunities for real-time data analysis and the exploration of other environmental policies, such as renewable energy subsidies and emissions trading systems. Ultimately, this research contributes to the understanding of how innovative modeling approaches can inform effective and equitable carbon taxation policies, driving sustainable economic growth in the face of climate change.