Unraveling the Complexity of Carbon Footprint Research: A Framework of Sigmoid‐Based Lifepaths, Regime Classification, and Topic Modeling

Our study introduces a novel bibliometric methodology integrating sigmoid‐parameterized lifepaths, regime classification, and topic modeling to analyze the dynamics of scholarly research. Using carbon footprint (CF) research as a case study, our approach moves beyond traditional bibliometric techniques by applying sigmoid modelling to track the research trajectories of authors, countries, and topics. This enables the identification of distinct phases, including acceleration, inflection, saturation, and decline. Our framework incorporates “time folds” to accommodate nonlinear disruptions and catalytic transitions, revealing how external factors – such policies, economic shifts, and global events – shape research progress. Our findings highlight regional disparities in research activity, with China and India exhibiting rapid acceleration, Western nations indicating saturation, and African research remaining fragmented. Topic modelling identifies key research shifts in agriculture, construction, and carbon‐free technologies, reflecting evolving global priorities. To facilitate further exploration, we provide an interactive visualization on GitHub, enabling scholars to engage with research lifepaths, analyze thematic shifts, and examine country‐level trends. By making these tools and methods openly accessible, this study offers a foundation for researchers to refine and expand the framework across other research fields, ultimately supporting deeper investigations into scholarly ecosystems, emerging trends, and the impact of research policies.