Year: 2025
Type: article
Abstract: Fuel consumption forecasting is a vital tool for energy planning, economic management, and public policy development. This study conducts a bibliometric analysis to identify trends, research fronts, and collaboration networks in the field of fuel consumption forecasting. A total of 5,025 documents from the Scopus database were analyzed using bibliometric methodologies, including keyword co-occurrence network analysis and S-curve projection. The results indicate an annual scientific production growth rate of 8.64% and suggest that the field may reach saturation by 2030. The findings highlight key research trends, such as the increasing use of artificial intelligence and machine learning to enhance predictive accuracy, as well as the integration of macroeconomic indicators like GDP and fuel price elasticity. Geographically, China, the United States, and India lead global scientific output, reflecting the strategic importance of fuel consumption forecasting in economic and environmental decision-making. The study also identifies gaps in interdisciplinary research and limited focus on integrating big data and real-time analytics into forecasting models. In conclusion, while fuel consumption forecasting has become a mature research field, further studies should explore emerging technologies and hybrid predictive models to improve accuracy and adaptability. These insights contribute to advancing methodologies and guiding future research agendas in energy management and policy-making.
Authors: Diego Ottoni De Araújo, Paulo Fernando Marschner, Eduardo Amadeu Dutra Moresi
Institution: Universidade Católica de Brasília
Language: English
Subfield: Economics and Econometrics
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W4409471676