Prediction Model for Carbon Emissions in Hebei Province: An Empirical Study Based on Boosting Algorithm

Abstract

In light of China’s ambitious "dual carbon" targets, accurate emission predictions are essential for effective reduction strategies. Traditional forecasting methods often fail to capture the complex nonlinear relationships in emission data, whereas Boosting algorithms enhance prediction accuracy by addressing these challenges. This study leverages Boosting algorithms, a state-of-the-art ensemble learning technique, to predict future carbon emissions in Hebei. By incorporating critical factors such as industrial output, energy consumption, and population dynamics, the study addresses the nonlinearities inherent in carbon emission data, which traditional forecasting models like ARIMA (Autoregressive Integrated Moving Average) and RF (Random Forest) struggle to capture. The results demonstrate that the Boosting model significantly outperforms conventional approaches, yielding the most accurate and robust predictions. This research offers valuable insights for shaping targeted carbon reduction strategies in Hebei, supporting its efforts to meet its carbon peak target, and contributing to China’s broader climate objectives. The findings underscore the potential of advanced machine learning techniques to inform data-driven decision-making in the pursuit of sustainable development and carbon neutrality.