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Artificial Intelligence and Sustainable Manufacturing Upgrading: Evidence from Manufacturing Export Technological Sophistication in Chinese Cities

Year: 2026

Type: article

Abstract: Upgrading manufacturing exports is essential for sustainable economic development. It strengthens productivity, resilience, and industrial competitiveness as cost-based advantages weaken. Yet it remains unclear whether city-level AI ecosystems are associated with this transition. Using a balanced panel of 257 Chinese prefecture-level cities from 2010 to 2023, this study examines the relationship between city-level AI ecosystem development, proxied by the stock of AI-related enterprises, and manufacturing export technological sophistication. Two-way fixed-effects models are combined with robustness checks, supplementary lagged and city-trend specifications, channel-related association analysis, heterogeneity analysis, and panel threshold models. The results show a positive within-city association between AI ecosystem development and export technological sophistication. AI ecosystem development is also positively associated with technological innovation and entrepreneurial vitality, while the positive industrial-structure association is weaker and sensitive to the inference method. The association varies descriptively across city types and becomes more pronounced after the local AI ecosystem crosses estimated thresholds. The study advances existing research by shifting from single-technology or firm-adoption measures to a city-level ecosystem perspective and by linking that perspective to export-structure upgrading. Theoretically, it highlights the role of absorptive capacity and complementary local capabilities; practically, it supports differentiated policies that match AI infrastructure, skills, and manufacturing capabilities to cities’ development stages. Because the evidence is observational, the estimates are interpreted as conditional associations rather than definitive causal effects.

Source: Sustainability

DOI: 10.3390/su18178931

Authors: Minhui Li, Lufang Kang, Fang Yu, Wusong Zhou, Wenxuan Zhao, Jie Qiu

Institutions: Beijing Normal University, Zhuhai, Beihang University, The Hong Kong University of Science and Technology (Guangzhou)

Language: English

Topic: Economic and Technological Innovation

Subfield: Economics and Econometrics

Field: Economics, Econometrics and Finance

Domain: Social Sciences

SDGs: Industry, innovation and infrastructure, Decent work and economic growth

Open Access status: gold

Estimated APC paid: $2,896