Unveiling emerging communities: a network approach on transport decarbonisation technology

Abstract

What are the latent thematic communities driving transport decarbonisation research across road, rail, maritime, and crossmodal domains? To answer this central question, this work proposes an integrated network model that fuses bibliographic coupling with Sentence-BERT semantic similarities, followed by a statistical analysis using state-of-the-art community detection algorithms. We assess partitions via four complementary metrics, modularity, silhouette, density, and NF1, and compare weighted versus unweighted graphs. Our analysis of mode-specific corpora uncovers distinct clusters: road research splits into urban last-mile automation and rural logistics; rail coalesces around a synchromodal scheduling hub with biofuel and corridor electrification offshoots; maritime divides into green fuels, autonomous safety, and shore-power streams; and crossmodal studies form overlapping triads of electrification, data analytics, and blockchain. Weighted edge integration uniformly enhances thematic clarity without altering algorithm rankings. These findings yield actionable algorithm-selection heuristics and tie each community to specific SDG targets, transforming aspirational goals into a concrete, community-by-community policy roadmap for green logistics.