Government Incentives for Emission Reduction in Blockchain Era: History
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Blockchain technology is very useful. Researchers considered the application of blockchain technology to smart contracts, green certification, and market information disclosure, and introduces the carbon trading market price as a parameter to solve the dynamic incentive problem of the government for port enterprises to reduce emissions under the carbon trading policy. 

  • blockchain technology
  • green port
  • emission reduction
  • carbon trading
  • optimal control theory

1. Introduction

Ports are the gateway of international trade and play a vital role in global economic and social development. However, the carbon emission pollution generated by ports should not be underestimated [1]. According to the statistics of China’s Ministry of Transport, China’s port cargo throughput in 2022 is about 15.685 billion tons. However, the carbon emissions of fossil fuels consumed by port enterprises each year are nearly 100 million tons, accounting for about 3% of the global greenhouse gas emissions. Therefore, port enterprises are facing increasing pressure of decarbonization, and port emission reduction is imminent [2][3]. To this end, the Chinese government has provided subsidies to encourage port enterprises to invest in green energy-saving and emission reduction technologies, such as replacing oil with shore power, liquefied natural gas (LNG) terminals, and clean energy trucks, in order to speed up the construction of green ports and alleviate port carbon emission pollution. Some powerful evidence is provided as follows: from 2016 to 2018, China’s Ministry of Transport awarded subsidies for the construction of port shore power facilities and the renovation of ship power receiving facilities; In September 2022, Guangzhou Port Authority issued the measures for the implementation of subsidy funds for ship emission control in Guangzhou port. In addition, governments of various countries have also begun to implement corresponding measures in the shipping industry, such as carbon emission trading policy. The evidence includes that the shipping industry will be included in the European Union Emission Trading System (EUETS) from 2024, and port enterprises such as Shanghai international port group and Shanghai Shengdong international container terminal have been included in the list of carbon emission quota management units of Shanghai in 2022. Therefore, under the government’s subsidy incentives and carbon trading policy, how to control and reduce the carbon emission of port enterprises, accelerate the investment and construction of green ports, and help the sustainable development of the shipping industry is the first research motivation.
As a world-changing and disruptive technology, blockchain technology is gradually being applied to the shipping industry, and port enterprises are benefiting from it [4]. There is much evidence here. For example, International Business Machines Corporation (IBM) and Maersk jointly built a shipping blockchain solution (TradeLens) to realize the digital operation of ports and shipping. Shanghai Port Group and China Ocean Shipping (Group) Company (COSCO) realize transparent and paperless operations with the support of blockchain technology. In 2021, Guangzhou Port successfully completed the docking with the electronic cargo release platform of the port and shipping blockchain, and all the main terminals were connected to the chain. Since 2022, the blockchain electronic cargo release platform of Shanghai Port has released a total of 335,000 bills of lading, totaling about 1.03 million TEUs, which effectively realized the cost reduction and efficiency increase in the logistics of imported cargo, and during the “Double 11” period, it even made a new history and realized the rapid release of imported e-commerce goods. The main reason why blockchain technology is so popular is that compared with traditional technology, blockchain technology has unique advantages. Specifically, it is a decentralized, point-to-point distributed database system, which is traceable, tamper-proof, open, and transparent. The traditional common technologies such as bar codes and radio frequency identification (RFID) tags may be copied and forged, and cannot be compared with blockchain technology in trusting the authenticity of its information processing [5]. Therefore, the second research motivation is to introduce the application values of blockchain technology such as smart contracts, green certification, and market information disclosure (which will be explained in detail later) in port enterprises’ emission reduction to improve the government’s subsidy incentives and carbon trading policy to stimulate and regulate port emission reduction.
In addition, although the existing literature has considered the investment and application of blockchain technology in the shipping industry (e.g., [4][6][7]), there are few studies on the government’s incentive contract design for port enterprises to reduce emissions by introducing the different application values of blockchain technology. Especially from the perspective of the principal–agent, based on the change in the status of port emission reductions and considering the carbon trading policy, there are even fewer studies on the dynamic incentive contract model of the government to port enterprises to reduce emissions. Therefore, based on the above realistic background of emission reduction of port enterprises in the shipping industry, three scenarios will be considered, namely, the scenario of no blockchain, the scenario where blockchain adoption without considering carbon trading policy, and the scenario where carbon trading policy is also considered under blockchain technology. Although there are many studies in the literature on different optimal control methods [8][9]. The advantage of the work is that researchers focus on the optimal control for carbon trading, and study the dynamic incentive of government for port emission reduction in the shipping industry from the perspective of the principal–agent.

2. Blockchain Technology in the Shipping Industry

Blockchain technology has become increasingly popular in supply chain management in recent years, which has attracted the attention of many scholars (e.g., Choi [10], Sun et al. [11], Shen et al. [12], Liu et al. [13], Guo et al. [14], Xu et al. [15]). Meanwhile, as an important carrier of cross-border trade, the research of applying blockchain technology to the shipping industry has also gradually become a hot topic [6]. The relevant research mainly includes two aspects. First, some scholars focused on the analysis of the application status and future development prospects of blockchain technology in the shipping industry. For example, Ying et al. [16] pointed out that by promoting the digitalization of the shipping industry, blockchain technology can help improve the operational efficiency of relevant enterprises involved and reduce the risks and unnecessary time costs associated with trade activities. To analyze the potential impact of new technologies such as blockchain on the performance and sustainability of the shipbuilding industry, Ramirez et al. [17] developed a performance model of the shipbuilding supply chain from an Industry 4.0 perspective, explored lean, agile, resilient, and green supply chain management modes, and proposed two phases to achieve the overall visibility and connectivity required for Shipbuilding Supply Chain 4.0. In order to explore the potential application fields of blockchain technology in port logistics management, Ahmad et al. [4] further discussed blockchain applications and architectures for port operations and logistics management. In addition, Pu et al. [18] presented a conceptual framework for the application of blockchain technology in the maritime industry, and they argued that it is crucial that managers should fully understand blockchain and its own specific issues and needs before adopting the technology. Subsequently, Balci and Surucu [19] and Kapnissis et al. [7] conducted empirical analysis on the adoption of blockchain in the shipping industry. They investigated the relationship between barriers to blockchain adoption, identified the main stakeholders of blockchain adoption in international trade in containers, and described the intention of the shipping sector to adopt blockchain technology.

Some other scholars are concerned about using blockchain technology to optimize the decisions of relevant enterprises in the shipping industry. For example, Meng and Wang [20] used game theory and mathematical planning methods to construct a benefit allocation mechanism for shipping industry alliance members to rent each other’s slots under blockchain technology, which optimized the allocation of slots among members and maximized the benefits of the alliance. Chen and Yang [21] used Stackelberg game theory to develop a mathematical model of a shipping logistics service supply chain consisting of shipping companies and freight forwarders, and found that the impact of freight rate competition on market evolution was reduced after blockchain application. Wang and Yin [22] constructed a pricing decision model for a secondary shipping supply chain under the traditional mode and blockchain technology mode, and explored the impact of different levels of information sharing on a private blockchain platform on the pricing and revenue of ports and carriers. In addition, Xin et al. [6] investigated the value of blockchain-based vertical cooperation dominated by ports or shipping companies in a one-to-two model of shipping service competition. They found that investments in blockchain technology can significantly increase the profits of shipping supply chain participants, and in particular, ports’ investments in blockchain technology led to more consumer surplus and social welfare. Meanwhile, Zhao et al. [23] integrated the technical features of blockchain decentralization with the investment choices of port and shipping supply chain members, and explored the issue of whether to centralize and whether to invest in a portfolio strategy in terms of shipping market prices and volumes and the economic effects of its shipping market.
However, the existing studies mainly focused on the pricing decisions and benefit distribution of port and shipping enterprises under blockchain technology and blockchain investment strategies in the shipping industry. Differing from them, the paper focuses on the dynamic incentive strategy of the government and the emission reduction investment (ERI) decision of the port under blockchain technology, especially researchers analyze the value and effect of blockchain technology in the government’s dynamic incentive contract.

3. Port Emission Reduction and Government Subsidy

The study is closely related to port emission reduction and government subsidy in the shipping industry, which is one of the important research topics in the field of shipping at present. Regarding the research on port emission reduction strategies, Acciaro et al. [24] argued that active energy management in ports could improve their service efficiency, promote the development of new alternative sources of income, and ultimately enhance their competitive position. Innes and Monios [25] analyzed ship docking data to calculate energy demand and found that installing cold ironing technology in medium-sized ports is feasible, which will consume less energy than traditional ships connected to shore power. Poulsen and Sampson [26] confirmed the existence of idle time in ports, detailed the reasons for it, and pointed out some previously overlooked factors. Wang et al. [27] studied the development process of port emission reduction from early “environmental factors and energy scheduling” to “low-carbon and green ports” through system review and Citespace visual analysis. Zhou et al. [28], based on the field theory in physics, combined with the characteristics of ship emission trajectory data, analyzed the spatio-temporal aggregation law of ship carbon emissions in the Wuhan Port.
Moreover, some scholars have taken into account the government’s regulatory and subsidy mechanisms in port emission reduction. Zhao et al. [29] considered a three-way evolutionary game model between the government, a port company, and another port company, and found that the environmental benefits can be maximized only if the government chooses passive regulation and the port company implements shore-side electricity. Zheng et al. [30] modeled two commonly used regulatory policies for port adaptation investments (minimum demand regulation and subsidies), making explicit the ambiguity in the probability of disasters and the policymaker’s attitude towards risk. Meng et al. [31] explored the impact of government regulation on cooperative emission reduction between ports and shipping companies by establishing a differential game model. They found that when the government only provides incentives to ports, if the port subsidizes shipping companies and the decision-making power is dispersed among shipping companies, the emission reduction effect is best, but it is unfavorable for port revenue. Meng et al. [32] constructed an evolutionary game model with the participation of the government, port enterprises, and shipping enterprises, and analyzed the evolutionary process of the selection of carbon reduction strategies among the three parties. Wang et al. [33] considered the interaction between governments, ports, and ships to develop a Stackelberg model to optimize government subsidy schemes to maximize the environmental benefits of unit currency subsidies. They found that in an optimal government subsidy structure, subsidies for ships should take precedence over subsidies for ports. Song et al. [34] constructed a Nash game between two shipping companies on shore rights usage decisions and analyzed the effect of government intervention on the equilibrium that can be achieved between the two shipping companies. Tan et al. [35] argued that the use of both environmental incentives and infrastructure subsidies mechanisms by the government influences port authorities to change the capacity decisions of port-specific terminals, which in turn affects the total emission reductions.
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, researchers considered 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.

4. Incentive Contract Design

The 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, researchers 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.

This entry is adapted from the peer-reviewed paper 10.3390/su151612148


  1. Alamoush, A.S.; Ölçer, A.I.; Ballini, F. Ports’ Role in Shipping Decarbonisation: A Common Port Incentive Scheme for Shipping Greenhouse Gas Emissions Reduction. Clean. Logist. Supply Chain 2022, 3, 100021.
  2. Dulebenets, M.A. Multi-Objective Collaborative Agreements amongst Shipping Lines and Marine Terminal Operators for Sustainable and Environmental-Friendly Ship Schedule Design. J. Clean. Prod. 2022, 342, 130897.
  3. Xiao, G.; Wang, T.; Chen, X.; Zhou, L. Evaluation of Ship Pollutant Emissions in the Ports of Los Angeles and Long Beach. J. Mar. Sci. Eng. 2022, 10, 1206.
  4. Ahmad, R.W.; Hasan, H.; Jayaraman, R.; Salah, K.; Omar, M. Blockchain Applications and Architectures for Port Operations and Logistics Management. Res. Transp. Bus. Manag. 2021, 41, 100620.
  5. Shen, B.; Xu, X.; Yuan, Q. Selling Secondhand Products through an Online Platform with Blockchain. Transp. Res. Part E Logist. Transp. Rev. 2020, 142, 102066.
  6. Xin, X.; Liu, M.; Wang, X.; Chen, H.; Chen, K. Investment Strategy for Blockchain Technology in a Shipping Supply Chain. Ocean Coast. Manag. 2022, 226, 106263.
  7. Kapnissis, G.; Vaggelas, G.K.; Leligou, H.C.; Panos, A.; Doumi, M. Blockchain Adoption from the Shipping Industry: An Empirical Study. Marit. Transp. Res. 2022, 3, 100058.
  8. Zhang, N.; Sun, Q.; Yang, L.; Li, Y. Event-Triggered Distributed Hybrid Control Scheme for the Integrated Energy System. IEEE Trans. Ind. Inform. 2022, 18, 835–846.
  9. Yang, L.; Li, X.; Sun, M.; Sun, C. Hybrid Policy-Based Reinforcement Learning of Adaptive Energy Management for the Energy Transmission-Constrained Island Group. IEEE Trans. Ind. Inform. 2023, 99, 1–12.
  10. Choi, T.-M. Blockchain-Technology-Supported Platforms for Diamond Authentication and Certification in Luxury Supply Chains. Transp. Res. Part E Logist. Transp. Rev. 2019, 128, 17–29.
  11. Sun, Z.; Xu, Q.; Shi, B. Price and Product Quality Decisions for a Two-Echelon Supply Chain in the Blockchain Era. Asia Pac. J. Oper. Res. 2022, 39, 2140016.
  12. Shen, B.; Dong, C.; Minner, S. Combating Copycats in the Supply Chain with Permissioned Blockchain Technology. Prod. Oper. Manag. 2022, 31, 138–154.
  13. Liu, S.; Hua, G.; Kang, Y.; Edwin Cheng, T.C.; Xu, Y. What Value Does Blockchain Bring to the Imported Fresh Food Supply Chain? Transp. Res. Part E Logist. Transp. Rev. 2022, 165, 102859.
  14. Guo, Q.; Zhao, P.; Cheng, S.; Ahmed, M. Two-Period Price Competition of Second-Hand Product Platforms with or without Blockchain under Different Supply and Demand Levels. Comput. Ind. Eng. 2023, 178, 109131.
  15. Xu, L.; Luo, Y.; Pu, X. Information Acquisition from Data-Driven Analytics: A Perspective of Blockchain Service in a Duopoly Market. Comput. Ind. Eng. 2023, 176, 108994.
  16. Ying, W.; Jia, S.; Du, W. Digital Enablement of Blockchain: Evidence from HNA Group. Int. J. Inf. Manag. 2018, 39, 1–4.
  17. Ramirez-Peña, M.; Sánchez Sotano, A.J.; Pérez-Fernandez, V.; Abad, F.J.; Batista, M. Achieving a Sustainable Shipbuilding Supply Chain under I4.0 Perspective. J. Clean. Prod. 2020, 244, 118789.
  18. Pu, S.; Lam, J.S.L. Blockchain Adoptions in the Maritime Industry: A Conceptual Framework. Marit. Policy Manag. 2021, 48, 777–794.
  19. Balci, G.; Surucu-Balci, E. Blockchain Adoption in the Maritime Supply Chain: Examining Barriers and Salient Stakeholders in Containerized International Trade. Transp. Res. Part E Logist. Transp. Rev. 2021, 156, 102539.
  20. Meng, Z.W.; Wang, X.G. Research on benefit allocation mechanism of shipping alliance transportation capacity sharing based on blockchain. Appl. Res. Comput. 2021, 38, 1631–1636. (In Chinese)
  21. Chen, Y.; Yang, B. Analysis on the Evolution of Shipping Logistics Service Supply Chain Market Structure under the Application of Blockchain Technology. Adv. Eng. Inform. 2022, 53, 101714.
  22. Wang, X.G.; Yin, M. Research on Two-stage Pricing of Shipping Supply Chain under Blockchain Platform. Comput. Eng. Appl. 2023, 59, 319–327. (In Chinese)
  23. Zhao, H.D.; Liu, J.G.; Wang, J.J.; Li, J. Research on investment strategy of shipping supply chain based on blockchain technology. J. Ind. Eng. Eng. Manag. 2022, 36, 117–128. (In Chinese)
  24. Acciaro, M.; Ghiara, H.; Cusano, M.I. Energy Management in Seaports: A New Role for Port Authorities. Energy Policy 2014, 71, 4–12.
  25. Innes, A.; Monios, J. Identifying the Unique Challenges of Installing Cold Ironing at Small and Medium Ports—The Case of Aberdeen. Transp. Res. Part D Transp. Environ. 2018, 62, 298–313.
  26. Poulsen, R.T.; Sampson, H. A Swift Turnaround? Abating Shipping Greenhouse Gas Emissions via Port Call Optimization. Transp. Res. Part D Transp. Environ. 2020, 86, 102460.
  27. Wang, B.; Liu, Q.; Wang, L.; Chen, Y.; Wang, J. A Review of the Port Carbon Emission Sources and Related Emission Reduction Technical Measures. Environ. Pollut. 2023, 320, 121000.
  28. Zhou, C.; Huang, H.; Liu, Z.; Ding, Y.; Xiao, J.; Shu, Y. Identification and Analysis of Ship Carbon Emission Hotspots Based on Data Field Theory: A Case Study in Wuhan Port. Ocean Coast. Manag. 2023, 235, 106479.
  29. Zhao, X.; Liu, L.; Di, Z.; Xu, L. Subsidy or Punishment: An Analysis of Evolutionary Game on Implementing Shore-Side Electricity. Reg. Stud. Mar. Sci. 2021, 48, 102010.
  30. Zheng, S.; Wang, K.; Li, Z.-C.; Fu, X.; Felix, T.S. Chan. Subsidy or Minimum Requirement? Regulation of Port Adaptation Investment under Disaster Ambiguity. Transp. Res. Part B Methodol. 2021, 150, 457–481.
  31. Meng, L.; Wang, J.; Yan, W.; Han, C. A Differential Game Model for Emission Reduction Decisions between Ports and Shipping Enterprises Considering Environmental Regulations. Ocean Coast. Manag. 2022, 225, 106221.
  32. Meng, L.; Liu, K.; He, J.; Han, C.; Liu, P. Carbon Emission Reduction Behavior Strategies in the Shipping Industry under Government Regulation: A Tripartite Evolutionary Game Analysis. J. Clean. Prod. 2022, 378, 134556.
  33. Wang, Y.; Guo, S.; Dai, L.; Zhang, Z.; Hu, H. Shore Side Electricity Subsidy Policy Efficiency Optimization: From the Game Theory Perspective. Ocean Coast. Manag. 2022, 228, 106324.
  34. Song, Z.; Tang, W.; Zhao, R.; Zhang, G. Implications of Government Subsidies on Shipping Companies’ Shore Power Usage Strategies in Port. Transp. Res. Part E Logist. Transp. Rev. 2022, 165, 102840.
  35. Tan, Z.; Zeng, X.; Wang, T.; Wang, Y.; Chen, J. Capacity Investment of Shore Power Berths for a Container Port: Environmental Incentive and Infrastructure Subsidy Policies. Ocean Coast. Manag. 2023, 239, 106582.
  36. Zhong, H.; Hu, Z.; Yip, T.L. Carbon Emissions Reduction in China’s Container Terminals: Optimal Strategy Formulation and the Influence of Carbon Emissions Trading. J. Clean. Prod. 2019, 219, 518–530.
  37. Yang, L.; Cai, Y.; Wei, Y.; Huang, S. Choice of Technology for Emission Control in Port Areas: A Supply Chain Perspective. J. Clean. Prod. 2019, 240, 118105.
  38. Zhong, H.; Chen, W.; Gu, Y. A System Dynamics Model of Port Hinterland Intermodal Transport: A Case Study of Guangdong-Hong Kong-Macao Greater Bay Area under Different Carbon Taxation Policies. Res. Transp. Bus. Manag. 2023, 49, 100987.
  39. Li, S.; Liu, J.; Wu, J.; Hu, X. Spatial Spillover Effect of Carbon Emission Trading Policy on Carbon Emission Reduction: Empirical Data from Transport Industry in China. J. Clean. Prod. 2022, 371, 133529.
  40. Wang, Y.; Liu, J.; Zhao, Z.; Ren, J.; Chen, X. Research on Carbon Emission Reduction Effect of China’s Regional Digital Trade under the “Double Carbon” Target—Combination of the Regulatory Role of Industrial Agglomeration and Carbon Emissions Trading Mechanism. J. Clean. Prod. 2023, 405, 137049.
  41. Holmstrom, B.; Milgrom, P. Aggregation and Linearity in the Provision of Intertemporal Incentives. Econometrica 1987, 55, 303.
  42. Zhou, X.; Swan, P.L. Performance Thresholds in Managerial Incentive Contracts. J. Bus. 2003, 76, 665–696.
  43. Yu, Y.; Kong, X. Robust Contract Designs: Linear Contracts and Moral Hazard. Oper. Res. 2020, 68, 1457–1473.
  44. Gao, X.; Tian, J. Multi-Period Incentive Contract Design in the Agent Emergency Supplies Reservation Strategy with Asymmetric Information. Comput. Ind. Eng. 2018, 120, 94–102.
  45. Gao, J.; Fan, H.; Cao, B.; Wang, N. Quality Incentive Contracts Considering Asymmetric Product Manufacturability Information: Piece Rate vs. Tournament. Comput. Ind. Eng. 2020, 144, 106446.
  46. Zhang, Y.; Xu, Q. Proportional Incentive Contracts in Live Streaming Commerce Supply Chain Based on Target Sales Volume. Electron. Commer. Res. 2023, 3, 1–29.
  47. Zhang, Y.; Xu, Q.; Zhang, G. Optimal Contracts with Moral Hazard and Adverse Selection in a Live Streaming Commerce Market. J. Retail. Consum. Serv. 2023, 74, 103419.
  48. Barbos, A. Dynamic Contracts with Random Monitoring. J. Math. Econ. 2019, 85, 1–16.
  49. Hori, K.; Osano, H. Dynamic Contract and Discretionary Termination Policy under Loss Aversion. J. Econ. Dyn. Control 2020, 111, 103794.
  50. Szydlowski, M.; Yoon, J.H. Ambiguity in Dynamic Contracts. J. Econ. Theory 2021, 199, 105229.
  51. Zhu, Y.; Liu, Z.; Wang, P.; Du, C. A Dynamic Incentive and Reputation Mechanism for Energy-Efficient Federated Learning in 6g. Digit. Commun. Netw. 2022, 1–10.
  52. Xie, Y.; Ding, C.; Li, Y.; Wang, K. Optimal Incentive Contract in Continuous Time with Different Behavior Relationships between Agents. Int. Rev. Financ. Anal. 2023, 86, 102521.
  53. Tan, R.; Wu, Y.; Su, P.; Liao, R.; Zhang, J. Optimal Dynamic Incentive Mechanism Design for Construction and Demolition Waste Recycling with Bayesian Learning. J. Clean. Prod. 2023, 412, 137371.
  54. Ma, D.; Hu, J.; Wang, W. Differential Game of Product–Service Supply Chain Considering Consumers’ Reference Effect and Supply Chain Members’ Reciprocity Altruism in the Online-To-Offline Mode. Ann. Oper. Res. 2021, 304, 263–297.
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