Identification of Research Trends in Forecasting Models for Sustainable Development and the Energy Sector

This study aims to identify, classify, and critically evaluate key research trends in forecasting models related to sustainable development within the energy sector. A structured query was applied to the OpenAlex bibliometric database, focusing on titles, abstracts, and keywords containing the terms forecasting, sustainability, and energy. This targeted approach ensured thematic relevance and minimised the inclusion of unrelated publications. The retrieved dataset underwent a multi-stage analysis using the Bibliometrix package in the R programming environment. Three forms of thematic clustering were applied: (1) based on thematic orientation, (2) according to the temporal evolution of research topics, and (3) reflecting emerging or declining development trends. To enhance methodological robustness, a triangulated approach was adopted, combining quantitative bibliometric mapping with qualitative sentiment evaluation. In the subsequent stage, negative sentiment analysis was conducted using a custom-developed tool. This alternative approach, focused on negative sentiment, enabled the identification of critical themes, including systemic barriers, technological limitations, and strategy inconsistencies that impede the advancement of forecasting models for sustainability in the energy domain. The proposed integration of sentiment analysis intensity into bibliometric review represents a methodological contribution to the field. The findings highlight structural and conceptual challenges while offering insight into future research trajectories. Addressing these barriers is essential for enhancing the practical relevance and strategic impact of forecasting models and is a managerial contribution particularly in the context of green jobs and sustainable strategic management.