The evolution of research in resources, conservation & recycling revealed by Word2vec-enhanced data mining

Resources, Conservation & Recycling (RCR) publishes original research in technological, economic, institutional, policy, and system-wide aspects of resource management and sustainability. Here we developed natural language processing (NLP) and Word2vec-based techniques to reveal for the first time the underlying patterns, interactions of research topics, and topical vectors and their connections with 10 resources covered in RCR based on all 4884 articles published since its inception. The 49 most trending-up, specific topics can be arranged into nine groups: general resources and materials, waste-based resources and materials, industrial management, human behaviors, analyzing methodologies, sustainable economy, climate-relevant, other sustainability, and other problems. The Word2vec-RCR model exhibits the distribution of topic vectors based on t-SNE, and the topics can be visually grouped into 13 major regions. The newly developed Word2vec model is proven to be effective for understanding evolution and interactions between research topics and defined subjects in RCR and broader environmental domains.