# Temporal Convolutional Network for Carbon Tax Projection: A Data-Driven Approach
## 5 Conclusions and Future Work
### Future Works  
Future work could extend this research in several directions:  
* Incorporating additional data sources: Integrating data on technological innovation, political factors, and international climate agreements could potentially improve the model's predictive power. * Exploring transfer learning: investigating whether models trained on data-rich countries can be effectively transferred to regions with limited historical data on carbon pricing. * Uncertainty quantification: developing methods to quantify and communicate the uncertainty in carbon tax price projections, which could be valuable for risk assessment and decision-making. * Comparative policy analysis: using the model to simulate and compare the potential outcomes of different carbon pricing policies across countries or regions. * Integration with economic models: exploring ways to integrate the TCN model with broader economic models to assess the macroeconomic impacts of carbon pricing policies [23]. While this study focused on carbon tax price projection, the potential applications of our TCN-based approach extend far beyond this specific domain. Future research could explore the following directions: (1) Environmental forecasting: The TCN model could be adapted for predicting other environmental indicators, such as greenhouse gas emissions, renewable energy adoption rates, or the economic impacts of climate change. For instance, it could be used to forecast the trajectory of global temperature changes or sea-level rises, providing valuable insights for climate adaptation strategies. (2) Energy market dynamics: Given its ability to capture complex temporal dependencies, our model could be applied to predict energy prices, demand, and supply across various sectors [24; 25]. This could include forecasting electricity prices in deregulated markets or projecting the adoption rates of electric vehicles. (3) Economic policy analysis: The TCN approach could be extended to analyze and predict the impacts of other economic policies, such as interest rate changes, trade policies, or fiscal stimulus measures. This could provide policymakers with a more nuanced understanding of policy effects over time. (4) Integration with macroeconomic models: An exciting avenue for future research would be the integration of our TCN model with computable general equilibrium (CGE) models or dynamic stochastic general equilibrium (DSGE) models. This integration could enhance the ability of these models to capture non-linear dynamics and complex interactions in economic systems. For example, a TCN-enhanced CGE model could provide more accurate projections of how carbon pricing policies impact different sectors of the economy over time. (5) Multi-regional analysis: The model could be adapted to simultaneously forecast carbon tax prices for multiple regions or countries, capturing cross-border effects and policy spillovers. This could be particularly useful for analyzing the global impacts of carbon pricing policies and informing international climate negotiations. (6) Scenario analysis for policy design: Building upon our uncertainty quantification methods, future research could develop more sophisticated scenario analysis tools. These could help policymakers design robust carbon pricing strategies that perform well across a wide range of possible future scenarios. (7) Hybrid modeling approaches: Exploring combinations of TCNs with other machine learning techniques or traditional econometric models could yield powerful hybrid approaches. For instance, combining TCNs with agent-based modeling could provide insights into how individual actors' behaviors influence and are influenced by carbon tax prices over time. These potential applications highlight the versatility of the TCN approach in addressing complex forecasting challenges across various environmental and economic domains.