Research on Policy Gaps in AI-Enabled Green Governance for China's Packaging Industry Based on the PMC Model — A Case Study: AIGC Packaging Practices at Zhejiang Great Shengda Packaging Co., Ltd.
Year: 2026
Type: article
Abstract: The packaging industry constitutes a vital component of China's manufacturing sector. By 2025, the number of enterprises above designated size in this industry will exceed 20,000, with annual operating revenue approaching 2 trillion yuan. Driven by both the "dual carbon" goals and intelligent manufacturing initiatives, the application of AI technology to enable green governance has emerged as a key pathway for industrial transformation. This study employs a mixed methodology combining policy text quantitative analysis with the PMC index model. First, we conducted word frequency analysis, co-occurrence network analysis, and thematic clustering on 47 relevant policy documents from 2018 to 2025 to reveal the structural characteristics of policy discourse. Subsequently, we constructed a PMC evaluation framework comprising nine first-level dimensions to quantitatively assess 17 core policies. Finally, we validated these findings using Great Shengda 's AIGC packaging practices alongside comparative cases, including Yutong Technology and Hexing Packaging. The study reveals that within policy texts, the two discourse clusters—"green" and "intelligent"—exhibit a distinct "dual-center–weak-linkage" pattern, with the cross-policy density between AI and green governance measuring only 0.06. The overall PMC score stands at 6.41, yet demonstrates a significant deficit in the "AI–Green Coordination" dimension, scoring merely 0.45. The research identifies three structural gaps and proposes policy recommendations, including establishing an "AI + Green" policy synergy framework, creating a dedicated fund for AI-enabled green transformation, and accelerating the development of cross-sector standard systems.
Source: Economics and Public Policy
DOI: 10.63313/epp.9058
Author: Kaiyi Fang
Institution: New York University
Language: English
Subfield: Strategy and Management
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W7225630767