Pathways for the Integration of Bioenergetic and Food Production: An Artificial Intelligence-Assisted Review

The application of agricultural biomass as a renewable energy source is garnering global attention, particularly for its contribution to the circular economy. However, the production pathways needed to harmonize agricultural production for alimentation and/or as a bioenergy resource remain unclear. Despite growing literature on renewable energy, a research gap persists in the integration of bioenergy into the food security framework. This research aims to identify the conditions under which bioenergy can be produced without jeopardizing food production. A methodology was developed that combined bibliometric clustering with generative artificial intelligence (GenAI), providing a systematic framework for sustainable agricultural biomass and bioenergy co-production. To determine potential pathways for integrated agricultural-bioenergy production, a bibliographic analysis was conducted using the Web of Science (WoS) database and VOSviewer software. A GenAI tool and diagramming software were then used to address the question: Is it possible to produce bioenergy without affecting food production? Two groups of keywords with the highest occurrence and citations, were obtained by data mining and clustering analysis. A total of 10 pathways were identified, supported by AI, able to balance bioenergy and food production. All pathways use agricultural crop residue, food waste, manure, algae, sludge, and torrefied biomass as energy sources. This study corroborates the use of GenAI to identify bioenergetic-food integration pathways and presents a novel methodology for identifying and integrating them into the current food security framework.