International Journal Of Engineering, Business And Management(IJEBM)

The expansion of filing scale, the decline in publicly listed invocation prices charged by token (Token), and the growth of R&D investment reflect the continued expansion of the visible supply of China’s artificial intelligence large models. However, these indicators cannot directly show how much sustainable industrial capacity public support has formed. In response to the problem that existing research emphasizes policy systems and industrial ecosystems while paying insufficient attention to policy effect determination, this paper proposes a three-tier evaluation framework of “outcome–causality–benefit”: first, identify the sustainable industrial capacity formed by a project; second, determine whether the relevant capacity is additionally induced by policy; and finally, compare the capacity increment and its knowledge spillovers, industrial chain and supply chain resilience, and real option value with real resource costs, external costs, administrative costs, and opportunity costs. To clarify the object of policy action, this paper distinguishes five types of enterprise decisions: intelligent computing power construction or procurement, foundation large model training and iteration, model adaptation and application services, model deployment and system integration, and application in production and operations and public service scenarios. This paper further proposes that policy evaluation should not be treated as a one-time acceptance check; rather, based on ex ante, interim, and ex post evidence, it should dynamically choose among continuing or expanding support, reducing support intensity or adjusting instruments, time-limited rolling support, and restructuring or exit. This paper thereby forms a conceptual industrial policy evaluation framework centered on capacity increment, causally based on policy additionality, and taking social net benefits as the ultimate criterion.