# Temporal Convolutional Network for Carbon Tax Projection: A Data-Driven Approach
## 1 Introduction  
Carbon taxation has emerged as a pivotal instrument in the global effort to mitigate greenhouse gas emissions and combat climate change. As emphasized by the Environmental Defense Fund and the Carbon Pricing Leadership Coalition, the effective implementation of carbon taxes necessitates clear objectives, stakeholder engagement, and regular monitoring [1, 2]. Central to this process is the accurate projection of carbon tax prices, which enables governments and businesses to anticipate future costs and make informed strategic decisions [3]. The importance of carbon pricing to climate policy cannot be overstated. As nations worldwide grapple with the urgent need to reduce greenhouse gas emissions, carbon taxes have proven to be an effective market-based mechanism to incentivize the transition to cleaner energy sources and more sustainable practices [4]. However, the effectiveness of carbon pricing policies heavily relies on the ability to accurately forecast future carbon prices, a task that has proven challenging, due to a complex interplay of economic, political, and environmental factors [5]. Carbon taxes operate on the principle of internalizing the external costs of carbon emissions, thereby aligning market prices with the true social cost of carbon-intensive activities. By putting a price on carbon emissions, these taxes create a financial incentivefor businesses and consumers to reduce their carbon footprint. This can drive innovation in clean technologies, encourage energy efficiency, and promote the adoption of renewable energy sources. However, the optimal level of carbon taxation remains a subject of debate, with economists and policymakers grappling with the challenge of balancing environmental goals with economic considerations. The dynamic nature of carbon pricing adds another layer of complexity to the forecasting challenge. Carbon tax rates are often subject to periodic adjustments based on factors such as emission reduction targets, economic conditions, and technological advancements. These adjustments can lead to significant fluctuations in carbon prices over time, making accurate long-term projections particularly challenging. Moreover, the global landscape of carbon pricing is diverse, with different countries and regions adopting varying approaches and tax rates, further complicating the task of developing a unified forecasting model. Traditional methods of time series forecasting, such as autoregressive integrated moving average (ARIMA) models, have been widely used in economic and financial predictions. However, these methods often struggle to capture the complex, non-linear relationships inherent in carbon pricing dynamics. They may fail to account for sudden policy shifts, technological breakthroughs, or global economic shocks that can significantly impact carbon prices. This limitation underscores the need for more sophisticated forecasting techniques that can adapt to the unique challenges posed by carbon tax price projection. Building upon existing research on carbon tax effectiveness [6] and the rationale behind carbon pricing policies [7], this paper introduces an innovative application of deep learning to the challenge of carbon tax price projection. Specifically, we employ a temporal convolutional network (TCN), a state-of-the-art deep learning architecture, to forecast carbon tax prices with unprecedented accuracy. TCNs have shown remarkable performance in various sequence modeling tasks, offering advantages such as parallel processing, flexible receptive fields, and a stable gradient flow. Our approach was motivated by a need to address the limitations of traditional forecasting methods in capturing the complex, non-linear dynamics of carbon pricing. By leveraging the power of TCNs, we aim to provide a more robust and accurate tool for policymakers and stakeholders for the development of effective carbon pricing strategies.