Carbon Price Forecasting Models: A Practical Overview

"Carbon Price Forecasting Models: A Practical Overview" provides a concise examination of methodologies used to predict carbon pricing trends within emissions trading systems and carbon tax frameworks. The overview categorizes forecasting models into fundamental, econometric, and machine learning approaches. Fundamental models analyze supply-demand dynamics, regulatory policies, and macroeconomic indicators to estimate future carbon prices. Econometric models rely on historical data and statistical relationships, applying time series analysis and regression techniques to capture price patterns and volatility. Machine learning models employ advanced algorithms, such as neural networks and support vector machines, to detect complex, nonlinear patterns in carbon market data. The practical overview highlights the strengths and limitations of each approach, emphasizing the importance of model selection based on data availability, forecast horizon, and market context. It also addresses challenges such as policy uncertainty, market liquidity, and external shocks, which complicate accurate price prediction. The summary underscores the growing role of hybrid models that integrate multiple techniques to enhance forecasting accuracy. Ultimately, this overview serves as a valuable guide for policymakers, market participants, and researchers aiming to understand and anticipate carbon price movements, facilitating better decision-making in carbon management and climate policy implementation.