我国即将进入城镇化后期阶段，党的二十大工作报告提出建设“节约能源、建设可持续发展的经济形态”城市，引领城镇化建设向智慧城市的方向发展。“智慧城市”是利用新一代信息技术手段，对城市居民生活工作、企业经营发展和政府行使行政职能的相关活动与需求进行智慧地感知、分析、集成和应对，为市民提供美好的生活和工作环境，为企业创造有利的商业发展环境，为政府构建高效的城市运营管理环境。PPP模式能够有效缓解政府财政压力。为解决智慧城市PPP项目中存在的风险管理水平不高问题，对智慧城市PPP项目尤其县域特殊场景 PPP 项目的风险管理研究具有重要的现实意义。

PL县作为中国县域智慧城市建设的先行示范区，于2024年1月启动总投资4.16亿元的智慧城市PPP项目，建设“1平台+2中心+N应用”。本文梳理了智慧城市的发展脉络、国内外PPP项目管理、风险管理方面的最新研究成果；全面介绍了PL县域智慧城市PPP项目建设内容、项目模式与运行流程、投资预算、资金筹措、投资回报机制等情况及风险管理现状，通过问卷调查，对风险管理存在问题及原因进行了分析；运用文献研究法和案例分析法，从立项、筹融资、建设到运营阶段，对PL县域智慧城市 PPP 项目的风险因素进行了全面识别，构建了项目全生命周期48项风险因素的初步风险清单，并采用德尔菲法，借助领域内资深专家的专业判断，筛选出24项关键风险因素，形成了5个一级风险、24个二级风险的清晰且实用的风险清单；基于AHP法，构建了风险评价模型，利用模糊综合评价法进行了风险评价，PL县域智慧城市PPP项目风险综合评价得分为42.806，处于“一般风险”程度，其中市场环境风险、运营成本超支风险、资本方退出风险3个风险为较高风险；工艺质量风险、设计方案风险、工程管理风险、运营需求变化风险、市场竞争风险、政策变动风险、建设需求变更风险、使用者付费意愿风险、资金配置混乱风险、利率风险、利益与风险分配机制不合理风险等11个风险因素是一般风险；根据风险评价的结果，本文提出了针对性的风险应对策略。

本文的研究有利于PL县域智慧城市PPP项目的顺利进行，实现项目各方利益最大化。也可为我国其他县域智慧城市PPP项目的风险管理提供有益的借鉴。

China is about to enter the late stage of urbanization. The report of the 20th National Congress of the Communist Party of China proposes to build cities with an "energy-saving and sustainable economic form" to guide the development of urbanization toward smart cities. A "smart city" uses new-generation information technology to intelligently sense, analyze, integrate, and respond to the activities and needs related to urban residents' life and work, enterprise operation and development, and government administrative functions. It aims to provide citizens with a better living and working environment, create a favorable business development environment for enterprises, and build an efficient urban operation and management environment for the government. The PPP model can effectively alleviate the financial pressure on the government. To address the issue of low risk management level in smart city PPP projects, studying risk management for smart city PPP projects, especially those in special county-level scenarios, is of great practical significance.

As a pioneer demonstration zone for county-level smart city construction in China, PL County launched a smart city PPP project with a total investment of 416 million yuan in January 2024, aiming to build a "1 platform + 2 centers + N applications" system. This paper combs the development context of smart cities and the latest research achievements in PPP project management and risk management at home and abroad. It comprehensively introduces the construction content, project model and operation process, investment budget, financing, investment return mechanism, and current risk management status of the PL County smart city PPP project. Through questionnaire surveys, it analyzes the existing problems in risk management and their causes. Using literature research and case analysis methods, it comprehensively identifies risk factors in the PL County smart city PPP project from the project approval, financing, construction to operation stages, constructs a preliminary risk list of 48 risk factors throughout the project's life cycle, and uses the Delphi method with the professional judgment of senior experts in the field to screen out 24 key risk factors, forming a clear and practical risk list with 5 primary risks and 24 secondary risks. Based on the Analytic Hierarchy Process (AHP), it constructs a risk evaluation model and uses the fuzzy comprehensive evaluation method to evaluate the risks. The comprehensive risk evaluation score of the PL County smart city PPP project is 42.806, indicating a "general risk" level. Among them, three risks—market environment risk, operation cost overrun risk, and capital provider exit risk—are of higher risk; 11 risk factors, including process quality risk, design scheme risk, project management risk, operation demand change risk, market competition risk, policy change risk, construction demand change risk, user payment risk, fund allocation chaos risk, interest rate risk, and unreasonable benefit and risk allocation mechanism, are general risks. According to the risk evaluation results, this paper proposes targeted risk response strategies.

The research in this paper is conducive to the smooth progress of the PL County smart city PPP project and the maximization of the interests of all project parties. It can also provide useful references for the risk management of smart city PPP projects in other counties in China.

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