Improving Carbon Emission Predictions with NLP and LSTM Integration

In recent years, the urgency of addressing climate change has intensified, necessitating accurate predictions of carbon emissions to inform policy decisions and mitigation strategies. This paper presents an innovative approach to improving carbon emission prediction models by integrating Natural Language Processing (NLP) techniques with Long Short-Term Memory (LSTM) networks. Initially, a baseline LSTM model is developed to forecast carbon emissions using historical emission data. Subsequently, we incorporate NLP-processed information extracted from relevant textual sources such as government reports, news articles, and scientific papers. Key features, including keywords, thematic distributions, and sentiment scores, are derived from the text and combined with numerical data to create a multi modal input for the LSTM model. Experimental results demonstrate that the inclusion of NLP-enhanced features significantly enhances the model’s predictive accuracy. This study underscores the potential of leveraging textual data to enrich traditional prediction models, providing a more comprehensive tool for understanding and forecasting carbon emission trends.