As a critical aspect of the industry 4.0 era, the application of artificial intelligence (AI) is significant to environmental governance. It serves as a crucial driving force in assisting enterprises in the transition toward low-carbon practices. This paper examines China’s A-share industrial enterprises from 2011 to 2022, constructs and trains a word vector model to extract AI-related terms, and the impact of AI applications on the carbon emission intensity of these enterprises is investigated. The findings reveal that enhancing the level of AI application can effectively decrease carbon emission intensity. Specifically, a 1% increase in AI application leads to a reduction of 0.0395% in carbon emission intensity. Further analysis indicates that enterprises can diminish their carbon emission intensity by the optimization of supply chain and green technology innovation. Heterogeneity analysis suggests that utilizing AI is beneficial for reducing the carbon emission intensity of manufacturing, high-tech, and high-pollution enterprises. The results of this study enrich the micro-level research on the relationship between AI and carbon emission intensity, offering valuable insights for enterprises aiming to achieve sustainable development.

This study compares two policy instruments for decarbonizing China’s seafood exports to the EU and UK over 10 years using a recursive dynamic computable general equilibrium model. One instrument applies tariff-like carbon surcharges on embedded emissions at the border. The other recognises certified low-carbon production through tiered rate reductions or exemptions. The model constructs product-level carbon cost wedges for processing electricity, aluminium packaging, and cold-chain operations, then transmits them to border prices through pass-through and to import volumes through Armington demand. These mechanisms operate inside a dynamic setting with capital accumulation, learning, and technology adoption. We evaluate processed tuna, shrimp, whitefish, and fresh tilapia to reflect differences in energy use, packaging intensity, and cold-chain reliance. Results show that certification, especially when paired with targeted domestic green finance or tax offsets, speeds adoption of cleaner power and refrigerants and preserves market share better than uniform surcharges. Effects differ between coastal and inland production hubs, supporting location-specific policy bundles. Sensitivity analysis varies carbon prices, adoption speeds, and certification coverage within stated parameter ranges. We report trade, export revenue, emissions, investment, and welfare outcomes and identify product and channel drivers of exposure.