The Transformative Role of Artificial Intelligence on Public Governance : A Comparison of Urban Traffic Management in the Chinese Cities of Xiong’an New Area, Hangzhou, and Daqing

Details

Supervisors

Aubin, David

Faculty

Faculté des sciences économiques, sociales, politiques et de communication

Degree label

Master [120] en administration publique, à finalité spécialisée

Abstract

enThe deep application of artificial intelligence (AI) in urban traffic governance is evolving from a simple technical tool into a systemic force that changes government governance structures, processes, and power relations. However, existing research largely focuses on the functional implementation of AI and increases in efficiency, paying insufficient attention to the internal mechanisms by which it drives the transformation of government governance models. This study takes “How does AI technology drive the transformation of government governance models?” as its core research question. It selects three Chinese cities—Xiong’an New Area, Hangzhou, and Daqing—as comparative cases, representing the “native construction,” “exploratory practice,” and “holistic transplantation” paths, respectively. Employing a qualitative comparative case study method, the study creates a four-stage dynamic analytical system of “initial conditions—driving mechanisms—institutional adaptation—application testing” to systematically examine the differentiated paths and basic patterns of government governance transformation following the embedding of AI in urban traffic governance. The research reveals that the differences among the three paths originate from different configurations of initial conditions: Hangzhou’s combination of “endogenous technology supply + authentic demand + strong market tension” leads toward a path of preemptive exploration; Xiong’an’s combination of “arranged technology supply + constructed demand + suppressed market tension” leads toward a path of synchronous planning; and Daqing’s combination of “dependent technology supply + survival-driven demand + degraded market tension” leads toward a path of delayed transplantation. The depth of institutional adaptation across the three paths displays a clear gradient: Hangzhou is the deepest (institutions + task forces + legislation + capital), Xiong’an is intermediate (enterprises + institutions + systemic breakthroughs), and Daqing is the shallowest (institutional gap-filling only). The study distills a “Three-Element Bypass Model”: building on Moore’s Strategic Triangle (Operational Capacity, Legitimacy and Support, Public Value) framework, it reveals how the structural deficit in operational capacity forces governments to “cede authorization” to technology companies, forming a “technology bypass,” and how this bypass, while delivering technical support, simultaneously generates authorization deficits, driving the government into a dynamic cycle of “cession—adaptation—re-cession—re-adaptation.” The core theoretical propositions of this study are: the government’s operational capacity deficit in public services in the AI era is structural; authorization cession is the inevitable result of operational capacity deficits; the new “authorization-support” pathway compels changes among government institutions; the structural gap between the speed of technology deployment and the speed of organizational adaptation constitutes a cross-systemic shared challenge in smart governance; and there is no optimal path for government transformation in the AI era—only different paths determined by initial conditions. The study offers theoretical observations and policy implications for understanding the reconstruction of government capacity, the redistribution of public-private responsibilities, and the adaptive transformation of governance models in the digital age, while also providing analytical tools and empirical references for cross-systemic comparative studies of smart governance.