This thesis examines how industrial policies targeting the New Energy Vehicle (NEV) sector influence regional economic development in China, specifically highlighting the varied impacts across different urban contexts. Using a comparative case study methodology, the research evaluates the effectiveness and quality of multiple central government interventions, including subsidies, tax incentives, and infrastructure investments. Integrating theoretical insights from New Institutional Economics, regional economic development theory, and institutional embeddedness theory, this study demonstrates that policies designed for the NEV sector effectively reduce market entry barriers and stimulate innovation and growth at the enterprise level. Through qualitative process tracing and quantitative analyses of corporate and regional economic data, the research reveals significant yet differentiated regional impacts. Detailed case analyses indicate that Shenzhen, benefiting from robust governance and advanced technological infrastructure, maximizes policy efficacy, rapidly becoming a national exemplar of NEV-driven economic growth. In contrast, Hefei demonstrates how strategic governmental intervention can overcome structural limitations, successfully fostering a dynamic industrial cluster through targeted investment and proactive policy implementation. Conversely, Changchun, a city deeply entrenched in traditional manufacturing, illustrates the constraints imposed by institutional inertia and conservative local governance, significantly limiting the effectiveness of identical national policies. The research identifies governance efficiency, institutional coordination across administrative levels, and the degree of infrastructure development as critical determinants of policy effectiveness. By systematically analyzing these elements, this study contributes to theoretical discussions on industrial policy and regional economic transformation and aims to provide policymakers and industry stakeholders with practical policy and administrative recommendations.