Sustaining the New Energy Vehicle Innovation Chain in the Pearl River Delta: A Hybrid Computational Intelligence Framework for Multimodal Big-Data Analytics

The new energy vehicle (NEV) industry anchors China’s transition towards sustainable mobility, and the Pearl River Delta (PRD) hosts one of its densest clusters. Yet the spatial organisation of the innovation system underpinning it remains poorly understood, because actors, data and methods are heterogeneous. This study develops a hybrid computational intelligence framework linking kernel density estimation, spatial autocorrelation, social network analysis and temporal exponential random graph models (TERGMs) in one reproducible pipeline, applied to 4036 innovation entities, 1260 co-applied NEV patents yielding 274 cooperating pairs among 290 organisations, and 187 policy documents (2009–2025). The chain exhibits a dual-core, core-periphery agglomeration (global Moran’s I = 0.538, p = 0.001): a Shenzhen-centred mass extending into Dongguan and a smaller Guangzhou core, against 14 low-low peripheral units. Its cooperation network expands rapidly without consolidating: cooperating organisations grow from 30 to 290 while density falls from 0.071 to 0.007; it stays sparse and modular, anchored by universities and flagship firms. Geographic proximity and organisational scale raise, and technological distance lowers, the odds of cooperation, while same-tier alignment is not significant once proximity is controlled. Contrary to expectations, cities with richer non-subsidy portfolios show weaker tie formation (β = −0.59, p < 0.001), and strengthening over time, whereas subsidy intensity is only weakly positive (β = 0.16, p = 0.067). These associations, stable across five specifications, are observational, not causal; they imply that the sustainability of the PRD’s NEV innovation system rests less on further concentration than on making existing proximity usable across municipal boundaries.