Artificial intelligence, capability transformation, and marine green development: empirical evidence from Coastal China

Major challenges emerge in efforts towards the global sustainable development of our oceans. However, systematic exploration of the mechanisms through which artificial intelligence (AI) influences marine green development remains scarce. This study develops an integrated technology-capability-context framework to examine how AI influences marine green development and its boundary conditions. Drawing on panel data from 11 coastal provinces in China over the period 2011−2023, this study employs two-way fixed effects models with Driscoll-Kraay standard errors for baseline estimation, Bootstrap mediation tests to identify transmission channels, moderation analysis to examine the enabling role of marine green finance, and AI-related online search attention as an instrumental variable to address endogeneity. Results reveal a significantly positive association: a one-standard-deviation increase in the AI index increases the Marine Green Development Index (MGDI) by 0.233–0.297 standard deviations. Mechanism analysis reveals two pathways—enhancing marine research and innovation and strengthening digital government capacity—with indirect effects accounting for 26.3% and 17.7% of the total effect (0.712), jointly contributing 44.0%. Moderation analysis demonstrates that marine green finance exerts a positive moderating effect, with a significant interaction coefficient of 0.461. Heterogeneity analysis shows that the promoting effect of AI is more pronounced in regions with a lower share of marine secondary industry output (coefficient 0.338) and insignificant in regions with a higher share. These conclusions remain robust after multiple robustness checks and endogeneity treatments. By uncovering the intrinsic mechanisms through which AI drives marine green development from a capability transformation perspective, this study extends interdisciplinary research at the intersection of AI and sustainable development. It provides empirical evidence and policy insights for coastal regions and maritime nations globally to coordinate AI strategies, optimise green financial instruments, strengthen marine science and technology research, and enhance digital governance capacity.

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Publication Details

Journal
Frontiers in Marine Science
Published
2026-04-15
DOI
https://doi.org/10.3389/fmars.2026.1816756
Primary Topic
Coastal and Marine Management
Type
article
Field-Weighted Citation Impact
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article

Artificial intelligence, capability transformation, and marine green development: empirical evidence from Coastal China

Chong Huang, Kai Zhang, Dasai Li
Frontiers in Marine Science
Coastal and Marine Management
article

Artificial intelligence, capability transformation, and marine green development: empirical evidence from Coastal China

Chong Huang, Kai Zhang, Dasai Li
article en

Abstract

Major challenges emerge in efforts towards the global sustainable development of our oceans. However, systematic exploration of the mechanisms through which artificial intelligence (AI) influences marine green development remains scarce. This study develops an integrated technology-capability-context framework to examine how AI influences marine green development and its boundary conditions. Drawing on panel data from 11 coastal provinces in China over the period 2011−2023, this study employs two-way fixed effects models with Driscoll-Kraay standard errors for baseline estimation, Bootstrap mediation tests to identify transmission channels, moderation analysis to examine the enabling role of marine green finance, and AI-related online search attention as an instrumental variable to address endogeneity. Results reveal a significantly positive association: a one-standard-deviation increase in the AI index increases the Marine Green Development Index (MGDI) by 0.233–0.297 standard deviations. Mechanism analysis reveals two pathways—enhancing marine research and innovation and strengthening digital government capacity—with indirect effects accounting for 26.3% and 17.7% of the total effect (0.712), jointly contributing 44.0%. Moderation analysis demonstrates that marine green finance exerts a positive moderating effect, with a significant interaction coefficient of 0.461. Heterogeneity analysis shows that the promoting effect of AI is more pronounced in regions with a lower share of marine secondary industry output (coefficient 0.338) and insignificant in regions with a higher share. These conclusions remain robust after multiple robustness checks and endogeneity treatments. By uncovering the intrinsic mechanisms through which AI drives marine green development from a capability transformation perspective, this study extends interdisciplinary research at the intersection of AI and sustainable development. It provides empirical evidence and policy insights for coastal regions and maritime nations globally to coordinate AI strategies, optimise green financial instruments, strengthen marine science and technology research, and enhance digital governance capacity.

Frontiers in Marine ScienceVol. 13
Hebei University of Engineering (CN), Hebei University of Economics and Business (CN), Shanxi University (CN), Shandong University of Finance and Economics (CN)
Openalex Percentile: Top 20%
Coastal and Marine Management
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