Regional Disparities and Dynamic Evolution of Marine Science and Technology Innovation in China

Abstract

The present study addresses two critical gaps in coastal innovation system research: (1) the absence of integrated frameworks for evaluating spatiotemporal efficiency and (2) the lack of empirical analyses on the factors contributing to interregional knowledge disparities in marine science and technology. While prior studies primarily focus on determinants of innovation efficiency, they often neglect regional heterogeneity and the evolutionary dynamics of technological advancement. Additionally, the commonly used two-stage DEA method fails to account for environmental influences and statistical noise, introducing potential biases. To enhance analytical accuracy, a three-stage bootstrap-corrected DEA model incorporating environmental controls was employed. Using panel data from 11 Chinese coastal provinces and municipalities spanning 2009–2020, spatial stratification patterns were quantified through Dagum Gini decomposition and innovation clustering dynamics were examined via geospatial autoregressive models (ArcGIS 10.8.1). The present research represents the first integration of spatial econometrics with efficiency decomposition in the domain of marine technology. The findings indicate that while marine innovation efficiency in China remains suboptimal, it exhibits a trajectory of progressive improvement, with eastern coastal regions demonstrating superior performance. The observed spatial disparities follow an “east-strong, south-weak” distribution, highlighting the necessity for region-specific policy interventions. Further, standard deviation ellipse analysis corroborates a persistent south-north distribution of innovation productivity, with Jiangsu Province serving as the gravitational center. The present study provides a replicable framework for monitoring and optimizing maritime innovation ecosystems in developing economies, offering valuable insights for policymakers to mitigate spatial stratification and improve knowledge spillovers.