近年来，区块链技术在供应链管理中越来越受欢迎，引起了许多学者的关注（例如，Choi [10]， Sun et al. [11]， Shen et al. [12]， Liu et al. [13]， Guo et al. [14]， Xu et al. [15]）。同时，作为跨境贸易的重要载体，区块链技术应用于航运业的研究也逐渐成为热门话题[6]。相关研究主要包括两个方面。首先，一些学者重点分析了区块链技术在航运业的应用现状和未来发展前景。例如，Ying等人[16]指出，区块链技术通过推动航运业的数字化，可以帮助提高相关企业的运营效率，降低与贸易活动相关的风险和不必要的时间成本。为了分析区块链等新技术对造船业性能和可持续性的潜在影响，Ramirez等人[17]从工业4.0的角度开发了造船供应链的性能模型，探索了精益，敏捷，弹性和绿色的供应链管理模式，并提出了两个阶段来实现造船供应链4.0所需的整体可见性和连接性。为了探索区块链技术在港口物流管理中的潜在应用领域，Ahmad等人[4]进一步讨论了区块链在港口运营和物流管理中的应用和架构。此外，Pu等人[18]提出了区块链技术在海运业应用的概念框架，他们认为管理人员在采用该技术之前应充分了解区块链及其自身的具体问题和需求至关重要。随后，Balci和Surucu[19]以及Kapnissis等人[7]对区块链在航运业的采用进行了实证分析。他们调查了区块链采用障碍之间的关系，确定了在集装箱国际贸易中采用区块链的主要利益相关者，并描述了航运部门采用区块链技术的意图。

其他一些学者担心使用区块链技术来优化航运业相关企业的决策。例如，孟和王[20]利用博弈论和数学规划方法，构建了区块链技术下航运业联盟成员相互租用对方名额的利益分配机制，优化了会员之间的槽位分配，实现了联盟利益最大化。Chen和Yang[21]利用Stackelberg博弈论开发了由船公司和货运代理组成的航运物流服务供应链的数学模型，发现区块链应用后运费竞争对市场演进的影响减小。王和尹[22]构建了传统模式和区块链技术模式下二级航运供应链的定价决策模型，并探讨了私有区块链平台上不同层次的信息共享对港口和承运人定价和收入的影响。此外，Xin等人[6]在一对二的航运服务竞争模式中研究了由港口或航运公司主导的基于区块链的垂直合作的价值。他们发现，对区块链技术的投资可以显着增加航运供应链参与者的利润，特别是港口对区块链技术的投资导致了更多的消费者剩余和社会福利。同时，Zhao等人[23]将区块链去中心化的技术特征与港口和航运供应链成员的投资选择相结合，从航运市场价格和数量以及航运市场的经济效应等方面探讨了是否集中化和是否投资投资组合策略的问题。

然而，现有的研究主要集中在区块链技术下港口和航运企业的定价决策和利益分配以及航运业的区块链投资策略。有不同之处在于，本文重点研究了区块链技术下政府的动态激励策略和港口减排投资（ERI）决策，特别分析了区块链技术在政府动态激励契约中的价值和效果。

我们的研究与航运业的港口减排和政府补贴密切相关，是目前航运领域的重要研究课题之一。关于港口减排策略的研究，Acciaro等人[24]认为，港口的积极能源管理可以提高其服务效率，促进新的替代收入来源的发展，并最终增强其竞争地位。Innes和Monios[25]分析了船舶停靠数据以计算能源需求，发现在中型港口安装冷熨烫技术是可行的，这将比连接到岸电的传统船舶消耗更少的能源。Poulsen和Sampson[26]证实了港口存在空闲时间，详细说明了其原因，并指出了一些以前被忽视的因素。Wang等人[27]通过系统审查和Citespace视觉分析，研究了港口减排从早期的“环境因素和能源调度”到“低碳绿色港口”的发展过程。Zhou等[28]基于物理学场论，结合船舶排放轨迹数据特征，分析了武汉港船舶碳排放的时空聚集规律。

此外，一些学者还考虑了政府在港口减排方面的监管和补贴机制。赵等[29]考虑了政府、港口公司和港口公司之间的三方进化博弈模型，发现只有政府选择被动监管，港口公司实施岸边电力，环境效益才能最大化。Zheng等人[30]模拟了两种常用的港口适应投资监管政策（最低需求监管和补贴），明确了灾害概率的模糊性和政策制定者对风险的态度。Meng等人[31]通过建立差分博弈模型，探讨了政府监管对港口和航运公司合作减排的影响。他们发现，当政府只向港口提供激励时，如果港口补贴航运公司，决策权分散在航运公司手中，减排效果最好，但不利于港口收入。Meng等[32]构建了政府、港口企业和航运企业参与的演化博弈模型，分析了三方碳减排策略选择的演化过程。Wang等人[33]考虑了政府、港口和船舶之间的相互作用，开发了Stackelberg模型，以优化政府补贴计划，使单位货币补贴的环境效益最大化。他们发现，在最佳的政府补贴结构中，对船舶的补贴应优先于对港口的补贴。Song等人[34]构建了两家航运公司之间关于岸权使用决策的纳什博弈，并分析了政府干预对两家航运公司之间可以实现的平衡的影响。Tan等人[35]认为，政府使用环境激励和基础设施补贴机制会影响港口当局改变特定港口码头的容量决策，进而影响总减排量。

In addition, in order to further manage the emission reduction of port enterprises, some other scholars have considered the government’s implementation of carbon emission policies for the shipping industry, such as carbon trading and carbon tax policies. Zhong et al. [36] studied the specific impacts of the carbon trading mechanism on the optimal emission reduction strategies of container terminals by taking Nansha Terminal in China as an example. Yang et al. [37] analyzed the choice problem of ports and shipping companies for low-sulfur oils and on-shore power under the carbon trading mechanism. Zhong et al. [38] found that a carbon tax policy is a relatively direct and effective incentive to drive multi-modal transportation in the port hinterland towards greening. Li et al. [39] explored the impact of government intervention on the carbon emissions trading market, and suggested that excessive government intervention would lead to the failure of the carbon market mechanism. Wang et al. [40] analyzed the relationship between digital trade and carbon emissions, as well as the moderating role of industrial agglomeration and carbon emission trading mechanisms on the effect of digital trade in reducing carbon emissions.

Although the existing research on port emission reduction and government subsidy in the shipping industry has yielded important results and progress, this paper considers smart contracts, green certification, and market information disclosure of blockchain technology, studies the government’s dynamic incentive problem for port enterprises to reduce emissions from the perspective of the principal–agent, and analyzes the value and effect of carbon trading policy under blockchain technology, which has not been covered in the related studies cited above.

Our study is also related to the research of incentive contract design in operations management, which is a hot issue of academic concern and has a wider scope of research. Holmstrom and Milgrom [41] first proposed the principal–agent model and laid the foundation for the study of incentive contracts and incentive mechanisms. Subsequently, many scholars began to design contracts such as linear and commission to resolve conflicts of interest between principals and agents in different industries. For example, in the past, Zhou and Swan [42] investigated the optimality of piecewise linear incentive contracts and found evidence of the role of performance thresholds by examining Chief Executive Officer (CEO) compensation data. Yu and Kong [43] considered the ambiguity in the distribution of effort-related outputs and demonstrated that piecewise linear incentive contracts are uniquely optimal among salesperson compensation contracts. Gao and Tian [44] extended the single-period incentive contract model to the multi-period incentive contract model to constrain the behavior of the firms and motivate the firms to make greater efforts. Gao et al. [45] considered outsourcing a manufacturer to a supplier and proposed a quality incentive contract with asymmetric product manufacturability information. With the rise of the live-streaming industry, Zhang and Xu [46] discussed proportional incentive contracts based on target sales volume in the context of the live commerce supply chain and studied the optimization of contract design based on principal generation theory. They found that the optimal solution of the proportional incentive contract exists and is optimal under certain conditions. Meanwhile, Zhang et al. [47] further considered the moral hazard and adverse selection issues in contract design, studied incentive contracts in the live-streaming supply chain under the information asymmetry of streaming influence and recommendation efforts, and revealed that equilibrium contracts depend on the priori beliefs of Pinbo suppliers about streamer influence.

Since changes in the market environment are often dynamic, the design of the contract between the principal and the agent may not always be static, so some scholars have carried out research on the dynamic incentive contract. For example, Barbos [48] carved out the optimal contract realized under stochastic monitoring in a stochastic dynamic setting where the type of agent cost varies over time. Hori and Osano [49] explored how the timing of compensation payments and contract termination are jointly determined in a continuous-time principal–agent model when the agent has loss aversion preferences and the principal has a discretionary termination policy. Szydlowski and Yoon [50] studied a continuous-time principal–agent model in which the subject is ambiguous and unwilling to influence the agent’s cost of effort, and this robust contract produces a pay performance that appears to be overly sensitive. Zhu et al. [51] proposed a dynamic incentive and reputation mechanism to improve energy efficiency and training performance in federated learning. Xie et al. [52] analyzed the optimal contract in continuous time under the principal–multi-agent moral hazard environment based on the behavioral relationship between agents, and gave the optimal contract for the generalized principal–agent dynamic problem based on the stochastic optimal control theory, analyzed the optimal behavioral choices of the agents and incentive mechanisms. Tan et al. [53], motivated by information asymmetry that makes it difficult for recycling companies to determine incentive strategies for collectors, formulated a dynamic moral hazard model and found that collectors are always motivated to voluntarily maintain a high-quality supply of C&D waste under the optimal mechanism.

Unlike the above research, this paper follows the relevant research on dynamic decision models (e.g., Ma et al. [54], Meng et al. [31]), considers that the port emission reduction market is uncertain, and based on the dynamic equation of port emission reductions, studies the dynamic incentive contract design of the government (principal) for port enterprise (agent) to reduce emissions in the blockchain era.