At the beginning of 2020， COVID-19 broke out. The epidemic had a major impact on China’s social stability and economic development， leading to economic downturn and increasing systemic risks in the financial system； At the same time， diversified finance is developing rapidly in China. It is also of great practical significance to investigate the systematic risk spillovers of diversified finance and traditional financial industries. Diversified finance refers to other emerging financial industries except traditional financial industries such as banking， securities and insurance. Based on the daily loss rate data of ShenyinWanguo secondary industry index （banking index， securities index， insurance index and diversified financial index）， CoVaR and Δ CoVaR model is used to measure the systematic risk among financial industries， examines the systematic risk spillover effect between diversified finance and traditional financial industries， and compares it with the systematic risk spillover effect between traditional financial industries. It is found that systematic risk spillovers between diversified finance and traditional financial industries weaken each other， which indicates that traditional finance and diversified finance play a role in mutually reducing systematic risk， but systematic risk spillovers between traditional financial industries strengthen each other. In addition， under extreme market conditions， the mutual weakening effect of systemic risks between diversified finance and traditional financial industries is stronger. During the same period， the level of systemic risk spillover between traditional financial industries has increased， and the increase caused by direct impact is greater than that caused by indirect impact. The research shows that the development of diversified finance not only provides more abundant financial service products for the real economy， but also hedges the systematic risk of traditional finance. The findings of this study are of great significance for the future development of China's financial market. China should continue to adhere to the supply side reform of the financial industry and vigorously develop diversified finance， especially science and technology finance. This will not only further enrich China's financial market and promote the development of the financial system， but also help hedge the systematic risks of the traditional financial industry and promote the healthy development of the entire financial system.

With the advancement of financial liberalization and financial integration， the transaction cost of global foreign exchange （FX） markets has been greatly reduced， and the cross-border investment has become more convenient. But at the same time， it has also promoted the risk contagion in the global FX network. Therefore， it is of great significance to study the connectedness change of the FX markets and the network topology evolution at different periods. Using the data of global FX rates of 33 currencies and seven macroeconomic variables from 2011 to 2021 on the pacific exchange rate service website， tail risk spillover network is constructed based on the LASSO‒CoVaR model to measure the currency risk interconnectedness. The network interconnectedness structure of global FX markets during the European debt crisis and the Sino‒US trade friction is emphatically studied， and the dynamic network connectedness is empirically analyzed from the system‒wide， sector‒conditional and individual‒level measures. The empirical results show that： （i） The total connectedness of global FX markets is at a high level during the crisis， and there is an obvious currency clustering effect； （ii） The currencies from Middle East and South America mostly acted as the risk‒recipients and their stability is weak， while the incoming risk strength （in‒strength） and outgoing risk strength （out‒strength） of currencies from Asia and North America are mostly at the same level and their stability is relatively strong； and （iii） The in‒strength and out‒strength of the US dollar and euro are relatively low， suggesting that the fluctuations of global FX markets do not necessarily come from the world’s dominant currencies. The Japanese yen and South Korean won are affected by the political and economic impact of the Sino‒US trade friction after 2018， and their risk spillover strength persistently increased. The risk it received from other currencies has enhanced remarkably， after the Renminbi joined the special drawing rights. Through a comprehensive analysis of the tail risk spillover network in global FX markets， the investment strategies are suggested that investors should be more cautious when investing in currencies from the Middle East and Latin America， and consider more about the impact of the connectedness between the currencies of the United States， Japan， and Europe when constructing an investment portfolio.

With the rapid development of China’s financial capital market， especially the trading stock market， some institutional investors （individuals or units） tend to take advantage of their own capital， information， authority or other advantages to pursue high returns and reduce losses by taking manipulative actions， such as creating market illusion， false trading， misleading trading， which seriously damages the interests of investors. All these manipulations pose a great threat to the stable operation of the financial market order. Thus， how to deepen the scientific understanding of manipulative behavior and improve the efficiency of supervision is a great challenge. The research of participants’ strategic behaviors in financial market regulation based on game theory has attracted much attention. However， most of the existing researches on financial market manipulation are based on classical game theory. Classical game theory usually assumes that the participants are completely rational and independent， and concentrates the strategy space of the participants in the real number domain. It ignores the possible entanglement between the participants in the payoff of strategy choice and the preference of strategy choice， which cannot effectively reveal the behavior rules of participants. In order to solve the above problems， based on the quantum game theory， the strategy choice of participants in extended to complex number space， and a quantum supervision game model is developed for trading stock market manipulation. On this basis， the evolution of strategic choice and entanglement of investors and regulators are analyzed. Meanwhile， the strategic preferences and behavioral rules of regulators and investors under different initial entanglement states are analyzed. The results show that： （1） After adopting the quantum strategy， the returns of both participants have been improved. It means that compared with classical games， quantum supervised games can achieve Pareto improvement of Nash equilibrium solutions. （2） The parameter in the quantum initial state， namely the degree of entanglement， is a key factor influencing the manipulation preferences of investors. The regulators can induce investors to prefer non-manipulation strategies by strengthening the entanglement between their strict supervision and investor strategy manipulation. Also， the regulators can implement dynamic regulatory strategies based on changes in the initial entangled state， thereby reducing regulatory costs while ensuring the stable operation of the market. Finally， through simulation analysis， how the initial entangled state affects the manipulation returns of investors， and then affects their manipulation behavior preferences if further studied. Accordingly， some countermeasures and suggestions are given to help regulators to improve the efficiency of market supervision.

Earnings communication conferences are important platforms for communication between listed companies and minority investors in China. In fact， due to the anonymity and low threshold nature of earnings communication conferences， their participants include not only minority investors but also analysts， potential investors， competitors， etc. Because management cannot anticipate the participants’ questions in advance， more “hard information” and “soft information” may be inadvertently delivered. Whether analysts， who are important information intermediaries in capital markets， draw on forward-looking statements （hereafter FLS） in earnings communication conferences when collecting， analyzing， and producing information， how these FLS influence analysts’ earnings forecasts， and which kind of FLS is more useful are the questions explored in this paper.Using textual information from 2007 to 2020， it is found that firms with more FLS disclosed in Earnings Communication Conference have more accurate analysts forecast afterwards. Experienced analysts benefit more from using FLS due to better information processing capability and easier access to resources. Forward-looking statements assist analysts in making more accurate forecasts when information asymmetry is more severe. Then a possible explanation is presented. With more FLS disclosed by the firm， sell-side analysts and institutional investors may increase their corporate visits to verify the information acquired. In this way， analysts forecast accuracy is improved. Furthermore， it is also found that performance related FLS are more likely to improve analysts forecast accuracy， due to higher credibility and direct relation with earnings.

With the advancement of economic globalization and financial integration， the risk correlation between the global foreign exchange market and China’s foreign exchange market has become more complex， and the importance of the foreign exchange market to China’s economic development， financial security and opening up has become increasingly prominent. In recent years， major events at home and abroad， such as China-Us trade frictions， the COVID-19 epidemic， the US Federal Reserve’s intensive interest rate hike， and the Russia-Ukraine conflict， have further exacerbated the spread of systemic risks around the world. In order to ensure the healthy operation of the financial market and the stable development of the foreign exchange market， it is necessary to effectively monitor the level of systemic risk in the foreign exchange market. Therefore， accurately measuring the systemic risk of the foreign exchange market has important research value.There are abundant researches on the measurement of systemic risk， but the measurement of systemic risk in China’s foreign exchange market is still relatively blank. Since there is no authoritative and unified definition of systemic risk， based on the existing definition and the characteristics of systemic risk， the systemic risk of foreign exchange market is defined as the sum of the individual risk and the contagion risk of the participants in the foreign exchange market. Under this definition， in order to measure the systemic risk of China’s foreign exchange market， Chinese foreign exchange market-making banks are selected as the main participants in China’s foreign exchange market， the important role of such players in the formation of systemic risk in China’s foreign exchange market is analyzed， and the formation mechanism of systemic risk in China’s foreign exchange market is summarized based on this. And then the systemic risk level of China’s foreign exchange market is effectively measured.Three channels of impact of external events on the foreign exchange market are put forward， with Chinese foreign exchange market makers as the core. Based on the three channels， three measurement mechanisms are put forward from the perspectives of "top-down"， "bottom-up" and "whether to consider scale and leverage"， and then three corresponding risk measurement methods are chosen： ΔCoVaR， MES and SRISK. At the same time， in order to take into account the increasingly important relevance dimension， the knowledge graph of China’s foreign exchange market is constructed， and the weighted degree centrality is constructed as the characteristic index of the knowledge graph network. Combining three types of risk measurement methods and network characteristics， three types of systematic risk measurement indicators of China’s foreign exchange market are proposed： KN-ΔCoVaR， KN-MES and KN-SRISK. In order to verify the validity of the measurement index of this paper， 13 major events at home and abroad are selected as verification， which are divided into long events and short events， and the effectiveness of the measurement index is illustrated by analyzing the sensitivity of the measurement index to the events.The weekly data from 2016 to 2022 are selected， including the central parity data of 23 currencies， the yield data of 18 foreign exchange market makers， and the data of 3 RMB exchange rate indices （CFETS， SDR， BIS）. The validity of systemic risk indicators in the foreign exchange market is verified by empirical evidence. The indicators and indicators under the characteristics of knowledge graph network can effectively measure the short-term and long-term systemic risk level of China’s foreign exchange market under shocks. The use of CFETS as the index of China’s foreign exchange market is more conducive to measuring the systemic risk of China’s foreign exchange market. On this basis， the systematic importance ranking of China’s foreign exchange market makers banks is analyzed， and it is found that in the long run， the four major state-owned banks and Bank of Communications are more important， while in the short run， local banks are more important. Through the simulation of event shocks， the changes in the ranking of the systemic importance of foreign exchange market makers under different impact intensities of the three types of shock channels are analyzed， and the rules are summarized and found as follows： Under the influence of long-term events， it is necessary to strengthen the supervision of high systemically important banks （such as CITB）， taking into account the supervision cost， the supervision level of low systemically important banks （such as CMSB） can be appropriately reduced.The proposed systematic risk measurement index of China’s foreign exchange market can effectively reflect the impact of major events and provide effective monitoring tools for foreign exchange market regulators. In this paper， the ranking of systemically important forex market makers and their changing characteristics are presented to provide decision-making suggestions for forex market supervision departments.

In the era of rapid digitalization， networking， and intelligent technologies， human society has transitioned into a "hyper-connected world" characterized by vast data， billions of interconnected devices， and intricate interaction networks. This transformation has led to an unprecedented increase in the complexity and uncertainty of economic and financial systems， with hyper-chain risk emerging as a critical concern. Hyper-chain risk is defined as the uncertainty where local events can rapidly diffuse and amplify across tightly interconnected subsystems， potentially leading to a global crisis. It aims to provide a comprehensive overview of the concept of hyper-chain risk， its underlying mechanisms， and the implications for socio-economic systems in this paper， while also offering insights on how contemporary complex system science， sociology， and economics can be integrated to address these challenges.It begins by tracing the origins of hyper-chain risk， which arise from the intricate interweaving of human and machine systems， as well as the interconnectedness of information. This type of risk is not confined to a single system or sector but spans across multiple domains， forming a complex network of risks. It highlights that traditional approaches to risk management are increasingly inadequate in dealing with the emergent phenomena resulting from this high degree of interconnectedness. It is argued that a deeper understanding of hyper-connectivity and the associated risks requires a multidisciplinary approach， incorporating concepts from complex systems theory， network science， and social-technical-economic interactions. The hyper-chain risk is distinguished from other types of risks， such as component risk， systemic risk， and cascading risk， emphasizing its unique characteristics： the rapid diffusion and amplification of local disturbances， the involvement of multiple interconnected systems， and the resultant global impact.To understand the interactions among elements within economic and financial systems， the network representation of hyper-chain risk is introduced， including single-layer， multilayer， and high-order interaction networks. A dynamic modeling framework is also developed for hyper-chain risk， examining from three perspectives： supply chain networks， institutional coordination networks， and cognitive spillover networks. How shocks propagate through these networks is elaborated on， leading to a sharp decline in system functionality or productivity， and various mechanisms of risk contagion are uncovered， including direct， indirect， and cross-layer contagion in multi-layer networks. Additionally， it discusses the self-fulfilling feedback effects triggered by changes in market participants’beliefs or behaviors.Furthermore， the importance of adopting a guided self-organization principle is discussed and decentralization strategies are appropriated to manage the complexity of hyper-connected systems. Decentralization is highlighted as a method to reduce single points of failure， distribute risk， and enhance the overall resilience of the system. The need for adaptive regulatory frameworks is also emphasized that can respond to the dynamic nature of these systems， as well as the necessity of improving the financial literacy and risk management capabilities of market participants. For the future research directions， it calls for a shift in research paradigms in this paper， embracing a more holistic and interdisciplinary approach that integrates complex system theory， social science， and economics. It advocates for the development of advanced analytical tools， such as agent-based modeling， system dynamics， and artificial intelligence， to better simulate and predict the behavior of economic and financial systems.In conclusion， it offers a novel viewpoint on the challenges posed by hyper-chain risk in the context of the hyper-connected world. It synthesizes insights from existing literature and proposes a new paradigm for understanding and managing risks in the hyper-connected world. By bridging the gap between complex system science， sociology， and economics， it seeks to stimulate discussions and foster a robust and adaptive approach to managing the hyper-connected complexities of modern socio-economic systems. Its contributions are expected to inspire further interdisciplinary research and inform policy-making， ultimately contributing to the stability and sustainability of the global economy.

Pledged inventory is one of the typical financing modes of supply chain finance （SCF）， and the fluctuation of pledge value is the main risk faced by SCF. Therefore， how to measure the risk of pledge value fluctuation is the focus of SCF's risk management. as many previous works have shown， Value at Risk （VaR）， which is mainly promoted by the Basel accord， is widely used in academic and industry for risk measures. However， the conventional VaR measures approaches have three challenges： 1） the true distribution of the data is not known， so the distribution assumption is prone to mistakes； 2） it is difficult to accurately describe the nonlinear relationship between variables； 3） the mixed frequency data is not fully utilized. To address the above issues， the combination of mixed data sampling （MIDAS） and support vector quantile regression （SVQR）， namely MIDAS-SVQR model， are first applied to improve the performance of pledge’s VaR measure. The novel approach uses kernel functions to deal with nonlinear relationships and directly outputs quantiles without any distribution assumptions； meanwhile， it uses MIDAS to process the mixed frequency data to increase the ability of the model to extract the information from the mixed frequency data. To illustrate the efficacy of our method， empirical studies on six representative pledges include steel， copper， lead， zinc， and tin. The data is collected from Wind （https：//www.wind.com.cn/） and covers the period from Jan 1， 2007 to August 31， 2021. Then， the proposed model is compared with the classical model （GARCH）， quantile regression （QR）， SVQR and MIDAS-QR in terms of Kupiec test， conditional converage test and VaR duration test. The empirical results are promising and show that our method （with the highest average P value of the three backtests across all samples） outperforms the others. Moreover， it is found that the quantile regression models generally perform significantly better than the GARCH models. In future， reversed （un）restricted MIDAS can be incorporated into SVQR to enable model to use more mixed frequency data. To this end， this is an interesting topic and we leave it for future research.

Common prosperity requires long-term and stable economic development to support， and artificial intelligence can create more wealth on the basis of the original means of production， and promote the economy to reach a singularity state to consolidate the material foundation for common prosperity. However， artificial intelligence will also lead to social problems such as unemployment and income inequality， and even impact residents' consumption and affect the steady economic growth. Therefore， it is necessary to explore the complex effects between artificial intelligence and household consumption and the economic singularity， as well as the moderating effect of redistribution policies on the realization of the economic singularity. In this paper， a complex nonlinear production function containing the development degree of artificial intelligence is used to construct a dynamic general equilibrium model containing artificial intelligence and redistribution policy， and numerical simulation experiments are used to study the impact of artificial intelligence on economic singularity and the effect of redistribution policy. The research results show that： （1） Without artificial intelligence， as my country's capital accumulation leads to a decline in the efficiency of the marginal product of capital， and the aging of the workforce reduces the number of laborers， economic development will fall into a continuous downturn， and it will be difficult to reach an economic singularity. （2） Artificial intelligence will enable my country's economy to reach a singularity state before 2070， and the earlier the artificial intelligence technology matures， the earlier the time node will reach the economic singularity. （3） Artificial intelligence promotes the early realization of the economic singularity by improving the intelligence and automation in the production process and the consumption of residents. （4） The redistribution policy will delay the arrival of the economic singularity， and for every 4% increase in the new tax rate， the investment rate will drop by about 2%， but the earlier the AI technology matures， the stronger the hedge against this adverse impact. In view of this， my country should vigorously develop "new infrastructure" to comprehensively promote the progress of artificial intelligence technology， optimize the redistribution policy system， increase the effective consumption of residents， and enhance the government’s comprehensive governance capabilities to achieve the goal of common prosperity.

Does good ESG performance enhance corporate value and promote sustainable development？ In the context of the "dual carbon" goal， companies need to comply with policy and institutional requirements to achieve external legitimacy. It is also necessary to actively transform into green and form competitive advantages. At the same time， external pressure from stakeholders motivates and forces companies to develop， thereby enhancing corporate value. Based on the data of A-share listed companies in Shanghai and Shenzhen from 2012 to 2021， the impact of corporate ESG performance on corporate value is analyzed from the perspectives of internal development and external pressure. The mechanism of internal development needs and external supervision is analyzed from the two paths of corporate innovation investment and analyst tracking， and further the synergy between the two in enhancing corporate value is examined. At the same time， the moderating effect of ESG fund holdings on corporate ESG performance and corporate value is analyzed， and the role of corporate ESG performance in enhancing corporate value is further expanded.It is found that　first， good ESG performance of enterprises promotes the enhancement of corporate value. After endogeneity and robustness testing， the conclusions remain reliable. Second， mechanism analysis shows that corporate ESG performance mainly promotes enterprise value enhancement by increasing corporate innovation investment and obtaining tracking from analysts. Third， the extended analysis shows that internal innovation investment and external analyst attention play a synergistic role in promoting the improvement of corporate value. In addition， ESG fund holdings have a positive moderating effect on the relationship between ESG performance and corporate value. The research in this study shows the importance of enterprises practicing the ESG concept. To this end， enterprises should be further encouraged to practice the ESG concept， actively promote green and low-carbon transformation， continuously improve their ability to create value， and help achieve China’s modernization and sustainable development.

The DEA-Malmquist index method combines the DEA method with the Malmquist index method， which does not need to set the specific form of the production function in advance， and is particularly suitable for the analysis of multi-input and multi-output production systems. Thus， this method has been widely used in total factor productivity measurement. However， for input-growth production systems， the missing data for some smaller or larger parts of inputs can cause the systematic bias in the DEA-Malmquist index measurement results. Therefore， from the perspective of production possibility set modification， it is important to study the techniques to modify and supplement missing data to modify the measurement bias of the DEA-Malmquist index method.In general， it is common that the input-output data of decision-making units （DMUs） keeps increasing with the continuous economic and social development. Moreover， the DEA-Malmquist index method is constructed based on the distance function， and the measure of the distance function has to be based on the C2R model， BC2 model and their corresponding inter-period comparison model. Therefore， when comparing DMUs across periods， there may be the cases of missing data for smaller inputs in the current period （data short tails） and missing data for larger inputs in the previous period （data short heads）. Among them， the missing data for smaller inputs will cause the problem of non-feasible solutions for the DEA-Malmquist index method， while the missing data for lager inputs will directly cause the overall bias of the calculation results of DEA-Malmquist index method. Therefore， in order to ensure the accuracy of the calculation results of DEA-Malmquist index method， it is necessary to construct a frontier restoration technique to modify the production possibility set in the traditional DEA-Malmquist index method.Through the empirical analysis， it is found that the method proposed in this paper can not only effectively modify the measurement bias of the traditional DEA-Malmquist index method， but also solve the problem that there is no feasible solution. In addition， the proposed method in this paper provides a new idea for the theoretical research of DEA-Malmquist index method， and also provides an effective tool for the measurement of total factor productivity.

Aiming at the trust problem in the international trade logistics， the blockchain technology is used to build the underlying architecture of the international trade logistics to achieve the disintermediated trust. However， the present researches about the application of blockchain technology don’t consider the participation of regulators， and it does not conform to the business rules of international trade logistics. The business entities of the international trade logistics correspond to the blockchain nodes when the blockchain technology is applied to the international trade logistics. The illegal information can’t avoid to be published if all the business entities write to and read data by themselves. Given consensus algorithm plays a decisive role in building trust， the blockchain consensus is studied considering the regulators participating in the business audit process.The blockchain consensus process and rule are elaborated on considering the participation of regulators. Based on the above， node reputation which is a combination of consensus performance and business performance is introduced to be as a basis for consensus node selection. Secondly， Stackelberg game method is used to build blockchain consensus incentive model. While the change of profit of block manager and consensus nodes in the different situations of considering the participation of regulators or not is analyzed. Lastly， the research results show that the model effective condition of supervision and node reputation. The specific contents of this paper are as follows：Firstly， there are many stakeholders involved in the international trade logistics. According to the effect and correlation of these stakeholders on the business， the business stakeholders are divided into three groups—relevant stakeholders， indirect stakeholders and regulator. In the blockchain application environment， the above-mentioned three groups correspond to the nodes of user nodes， consensus nodes and the regulation nodes. Meanwhile， the identity and function of the business stakeholders are illustrated during the blockchain consensus process.Secondly， the consensus process and rule are elaborated on considering the participation of regulators. Specifically， the consensus rules include the audit content rule， the dynamic change rule for the number of consensus node and the dynamic selection rule of the consensus node. And the consensus process contains publishing trade information， trade review， determining consensus participation nodes， trade consensus validation， gaining consensus， block generated and trade data saved to the blockchain. Based on the above， node reputation is introduced to be as a basis for consensus nodes selection. The calculation of the node reputation considers four aspects： historical and recent consensus performance of the node， history smart contract performance of the node the cumulative freight volume of the node and the participation of the node for illegal trade. Elaborate here， the top-ranking node among the final consensus nodes is the block manager while the others are the consensus nodes.Lastly， Stackelberg game method is used to build blockchain consensus incentive model， and the change of profit of block manager and consensus nodes is analyzed in the different situations of considering the participation of regulators or not. Specifically， the block manager is the leader who deciding the price of the consensus resource and the consensus nodes are the followers who decide the consensus resource input. And the model effective condition of supervision and node reputation is summarized.It can extend the blockchain consensus from technology to participant in this paper. And it provides a reference for considering the role and function positioning of regulatory in blockchain， and it has enlightening function in the realization of regulation-friendly blockchain system.

Online social networks have been integrated into people’s lives， and the Internet has become an ideal platform for disseminating information. Internet Word of Mouth Marketing （IWOM） is becoming increasingly attractive to companies. However， the dissemination of some promotional information can generate a large number of negative comments， damaging reputation and long-term profits. Therefore， it is of great significance to study information dissemination strategies so that beneficial information can be better promoted. The node selection strategy is investigated to maximize the amount of net positive information diffusion among individuals in signed online social networks. Given a signed online social network G=(V,E,X,P,C)， each edge e\in E has an edge symbol x\in X and a propagation probability p\in P. C is the intensity of information interaction between individuals. The information propagation model is defined as the Signed Independent Cascade （SIC） model in signed networks. The problem of net positive information maximizing is to select a seed set containing k nodes from the node-set V and its assignment function F:S\to \{+,-\} so that the amount of positive information minus the amount of negative information is maximum at the end of the spread. The problem is shown to be NP-hard and its objective function computation is #P-hard. Second， it is demonstrated that the problem is neither submodular nor supermodular due to the combined effect of information. Then， according to the propagation characteristics， the propagation path and its approximate calculation method are proposed. The problem is further constructed to solve a positive monotone submodular function. Third， an efficient maximum coverage greedy algorithm is designed to maximize the amount of net positive information. Finally， experiments conducted on real networks to verify that the proposed algorithm is superior to other methods and show that information diffusion is not equal to the dissemination effect.

In the era of digital economy， with the application of big data and artificial intelligence technology， data has become an important strategic element for firms to compete. On the one hand， firms can provide consumers with targeted recommendations or innovative products； on the other hand， firms implement the “big data discriminatory pricing” to grab consumers’surplus. With this in mind， the controversy between data utilization and data protection becomes more and more extensive， which also leads to differentiated privacy protection policy， namely no protection policy， voluntary protection policy and mandatory protection policy.The existence of the privacy protection policy brings severe challenges to firms’ pricing decisions and data usage decisions. If the mandatory protection policy is implemented， the data application behavior of firms is strictly forbidden， and firms have to conduct unified pricing. If the voluntary protection policy is implemented， consumers can choose whether to disclose personal data to firms. If the no protection policy is implemented， the high ability enterprise can conduct the first-degree price discrimination.The price competition of firms is analyzed with different data usage capabilities based on the horizontal differentiated duopoly model， to solve the pricing equilibrium and social welfare under the different protection policy， and to clarify the implementation conditions of the different levels of protection policy. The main conclusions are as follows： （1） The consumer surplus maximization and producer surplus maximization can’t be achieved simultaneously， and the second-best can be achieved under no protection policy and mandatory protection policy. （2） When the data usage advantage is low， the consumer surplus is maximized under the voluntary protection policy， otherwise， the producer surplus is maximized. （3） Different from the previous studies which claim that price discrimination worsens the profits of firms， enterprise can enlarge the market scale through data utilization.The important theoretical innovation of this paper is to provide an insightful framework， find some reasonable results which are new to the existing literature， and supplement the relevant literature in the fields of data protection and data utilization. The data protection is necessary， but not absolute. The implement of data protection policy needs to rely on the data utilization ability of different industries and consumers privacy concerns.

The rapid development and transformation of the economy and society have led our country to gradually enter a high-risk society. With citizens’increasing awareness of risks and rights， the NIMBY （not in my backyard） effect has become a significant source of social stability risk. Managing the NIMBY risk chain， which involves transforming “actual risk” into “perceived risk”， is crucial for preventing the externalization of NIMBY syndrome into social stability risk. Based on the asymmetry of actual risk information， it aims to design the optimal risk disclosure mechanism for the administrator to address NIMBY syndrome. The residents’ private risk information sources， global network externalities of residents’ behaviors， and trust-driven learning rule are considered to model two typical communication scenarios in NIMBY events： strategic interactions among residents and risk disclosure from administrator to residents.In this way， a network communication and persuasion model is developed based on the Network Cheap talk and Bayesian persuasion theories. It is assumed that the administrator’s disclosure occurs after residents’ private information learning and strategic communication within community. Thus， a Network Cheap talk model is constructed to depict the strategic communication process among heterogeneous residents， deriving the perfect Bayesian equilibrium and analyzing the consensus on community risk beliefs under the equilibrium condition， as well as changes in consensus based on different private information realizations. Based on this， a public risk disclosure mechanism is designed for the administrator. Since residents’ private information serves as uncertain information for the administrator， the optimal mechanism must implement differentiated recommendations for residents based on different private signals while satisfying the incentive compatibility condition. Additionally， drawing from conservative Bayesianism， an affine distortion approach is employed to characterize resident’s trust-driven learning rule.The findings indicate that the optimal public risk disclosure mechanism is a deterministic interval threshold recommendation mechanism， providing incentive-compatible differentiated recommendations based on residents’ private information. The more optimistic the residents， the broader the recommended risk acceptance interval. Meanwhile， the negative intensity and number of private information sources determine whether and how administrator should disclose risk and the effectiveness of such disclosure. Additionally， residents’ trust positively influences the mechanism’s persuasive effect. However， the expected payoff of pessimistic residents （high private information environment） is a decreasing function of trust， leading to a non-increasing relationship between trust and social welfare.The optimal risk communication mechanism for administrator and offers specific suggestions are offered to enhance communication effects from private information sources is clarified and trust management. Also， the model developed in this paper contributes to the non-Bayesian persuasion theory under the persuasion scenario considering one-to-many， continuous state space， and strategical communication among receivers.

Under the background that sustainable development has become one of the major strategies of modernization， abiding by ESG principles has become one of the important conditions for the success of mega projects. The waste utilization of mega projects has significant social， environmental and project governance benefits， which is in line with ESG concept. It is of great theoretical and practical significance to explore the role of tunnel spoil utilization on ESG objectives of mega projects. Tunnel spoil has the characteristics of large quantity and various types， while the utilization of tunnel spoil has the characteristics of diversification and wide impact， which leads to the complexity of decision-making on the combination of spoil utilization methods. How to explore the combination with the highest ESG contribution from numerous combinations of tunnel spoil utilization methods is the research question to be addressed in this research.There are many constraints in the optimization of tunnel spoil utilization combination， and there are multiple objectives in ESG and economic aspects， leading to a multi-objective optimization problem. Therefore， multi-objective optimization methods are adopted in this research. After systematically sorting out the role of waste utilization on ESG objectives of mega projects and its internal mechanism， an ESG-oriented optimization framework of tunnel spoil utilization combination is proposed. The framework establishes a mapping relationship between the "combination of tunnel spoil utilization methods–environmental and social objectives–project governance objectives"， and transforms the optimization problem of tunnel spoil utilization combinations into a multi-objective optimization problem between the environmental and social objectives， project governance objectives and economic costs.Based on the developed multi-objective optimization model， an optimization method–improved adaptive NSGA-II （I-A-NSGA-II） is proposed by introducing fast non-dominated ordering， elite strategies， adaptive crossover and mutation and improved congestion distance computation. I-A-NSGA-II provides a reference for the decision-making of spoil utilization in mega projects. The utilization of tunnel spoil of a mega project is taken as a reference， and constructs simulation data as an arithmetic example to test the feasibility and effectiveness of the proposed method. The connotation of ESG in spoil utilization of engineering projects is defined and the application scenarios of ESG theory is expanded. Meanwhile， the research focus of tunnel spoil utilization is led from the evaluation of a single method or the comparison of multiple methods to the optimization of the combination of multiple methods， which enriches the decision-making research of spoil utilization in mega projects.

In recent years， performance-based warranty has become a new business model in the field of after-sales market for complex products. Under a performance-based warranty， manufacturers guarantee the product performance to enhance customer satisfaction. Therefore， the pursuit of high performance has become the common goal of manufacturers and users. At the same time， the rapid development of the new generation of information and communication technology has created conditions for real-time monitoring of product performance. In order to further optimize the user experience and reduce the downtime loss caused by frequent maintenance， a performance-based warranty policy design and optimization method is proposed with repair number limit. Under the proposed performance-based warranty policy， if the actual performance level hits the performance guarantee level， the manufacturer will implement replacement or imperfect repair according to the failure time of the product. Additionally， if the number of repairs exceeds the specified limit， the manufacturer is also required to compensate the user. Based on that， the critical performance degradation process is established with a replacement-maintenance strategy and the expected warranty cost is derived. Then， a performance-based warranty policy optimization model is constructed with the goal of maximizing the manufacturer’s profit. The Nelder-Mead method is used to obtain the optimal product price， warranty period length and performance guarantee level. The results show that the performance-based warranty policy can help the manufacturer to obtain higher profits compared with the traditional warranty policy. In addition， the analysis of the key parameters revealed that for manufacturers， slowing down the rate of product degradation， reducing the cost per repair and choosing a reasonable replacement interval play a key role in increasing profits.

In order to effectively solve the “contactless” distribution problem of fresh product under the background of sudden infectious diseases， taking into account the impact of the infectious diseases， commodity perishability， temperature control characteristic， site location and coordinated delivery routing planning of vehicle-drone， the location-routing problem of fresh product distribution based on infectious disease background is proposed. A bi-objective optimization model is constructed to minimize the total cost of logistics operation and the value loss of fresh product. Then， an efficient two-phase hybrid heuristic algorithm based on the improved K-means clustering and the extended Non-dominated Sorting Genetic Algorithm-II （ENSGA-II） is devised according to the problem characteristics. The improved K-means three-dimensional clustering algorithm solves the location problem of the distribution site by performing iterative optimization within the clustering scheme and among different clustering schemes； The ENSGA-II hybrid algorithm generates the initial solution through the scanning operator， controls the search for the Pareto optimal frontier based on the NSGA-II main loop framework， and embeds flexible storage structure and corresponding tabu search criteria to solve the problem of vehicle-drone routing problem. Finally， based on the case analysis， the location strategy of distribution site and the coordinated delivery route of vehicle-drone are obtained. According to the sensitivity analysis， the optimal temperature control setting and drone load capacity selection for fresh product distribution are obtained. And through a comparative analysis with \epsilon -constraint method， MOPSO algorithm and NSGA-II algorithm， the validity and feasibility of the bi-objective optimization model and the two-stage hybrid heuristic algorithm are verified. The research results show that an optimal vehicle-drone distribution plan can effectively reduce the objective values and realize the “contactless” delivery for ensuring the safety of vehicles and personnel in non-epidemic areas. A new theoretical basis and method reference is provided for the decision-making for the location-routing problem of fresh product distribution.

In the past emergency rescue of large-scale emergencies， the organizers mostly paid attention to the transportation cost and time cost in the distribution process and the goal was to reasonably transport as many materials as possible to the disaster-stricken area in the fastest time. In the new economic period， rescue activities no longer only focus on the material level， but also spiritual level. After a disaster， the victims will experience unstable psychological factors and may have risk perception due to the lack of materials， resulting in panic psychology. Such psychological factors are likely to have adverse social effects or even cause serious social problems. Moreover， in the early stage of rescue， the material reserves are limited， and it is difficult to meet the needs of all disaster-affected points for various types of materials through one delivery. Therefore， some important topics that how to consider the psychological factors of the victims in an uncertain environment and how to reasonably carry out multi-period dynamic allocation of limited resources need to be studied urgently. The prospect theory is integrated into the scheduling problem and a risk perception function is established to describe the psychological risk perception of the victims for obtaining relief materials. Considering the difference in the urgency of material demand in different disaster-stricken points， to make the disaster-stricken points with high demand urgency to be dispatched preferentially， a psychological risk perception function of the victims is established that considers the urgency of demand. In terms of timeliness， a linear time satisfaction function is used to establish a time satisfaction function that considers the urgency of demand. Because of the uncertain factors existing in the early stage of emergencies， the estimated values of the parameters are obtained utilizing expert estimation when the data is difficult to obtain. Taking into account subjective uncertainty， uncertain variables are introduced to describe them. Based on this， a multi-objective mixed-integer uncertainty programming model is established， aiming at the minimum psychological risk perception， maximum time satisfaction， and minimum transportation cost. Based on obtaining the expert's estimation information， the least squares principle is used to solve the expected value and variance of each uncertain parameter. Finally， the uncertainty theory is used to transform the uncertain programming into its deterministic equivalent form.An example based on COVID-19 is designed. The demand for daily necessities is positively correlated with the number of people affected by the disaster. According to the spread of infectious diseases， the number of infected people always shows a trend of increasing first and then decreasing gradually. The demand for materials also shows a trend of rising first and then falling； the transportation delay coefficient and damage coefficient are also set according to this trend. The material distribution and scheduling problems under emergency conditions are subject to severe time pressure， and to be able to quickly solve the parameter changes there are higher requirements for model solving efficiency and the requirements for the accuracy of the solution results are relatively loose. Since this model involves multiple cycles， multiple materials， multiple disaster-affected points， and multiple distribution centers， the algorithm for solving the model should follow the principles of fast convergence， low resource occupation， and strong robustness. In this paper， an improved artificial bee colony algorithm is designed to solve the model. Finally， through the analysis of the parameters and distribution plan， the following conclusions are drawn： ①The model can effectively control the transportation cost while considering timeliness， fairness， and the psychology of the victims； ②The psychological risk perception characteristics of the victims in different disaster scenarios are quite different when the disaster situation is more serious， the panic of the disaster victims is more serious， and the risk perception brought by the same proportion of unmet materials will gradually increase with the increase of the loss sensitivity coefficient and loss avoidance coefficient； ③The setting of the proportional fairness coefficient can effectively prevent the disaster-stricken points that are less affected by the disaster and the number of affected people can only be allocated a very small amount of materials， and reasonably control the fairness of the system. ④Decision makers can choose their attitudes toward the cost and psychological loss of victims through different combinations of decision preference coefficients， to form a multi-cycle emergency material distribution plan.The innovation of this paper is that， the prospect theory is used to establish the psychological risk perception function of the victims that considers the urgency of demand and the cognitive biases that the victims may have； In the case of incomplete information and no reference historical data， the estimated values of parameters are obtained by expert estimation， and uncertain variables are used to represent such parameters with subjective uncertainty； Considering the dynamic changes in supply and demand of multi-period materials， the proportional fairness coefficient is used to flexibly adjust the overall fairness of emergency material allocation； Finally， aiming at improving the timeliness of the arrival of materials， reducing the psychological risk perception of the victims and controlling the transportation cost， a multi-objective optimization model of emergency material distribution considering the psychology of the victims is proposed for multi-period， multi-stricken regions， multi-distribution centers， and multi-type materials.

How to set the location of a park-and-ride facility and the pricing of shared parking spaces in the era of autonomous vehicles to achieve the desired goals is studied. A linear city is supposed to stipulate that the private cars with solo travelers are not permitted to enter the city center. Travelers can only choose the park-and-ride mode or take the shared autonomous vehicle to the city centre. As vehicles could park themselves automatically with the help of automatic driving technology， there are three ways to park the vehicles： park the vehicle at home， park the vehicle in the free parking spaces near the park-and-ride point， park the vehicle in the shared parking spaces provided by the residents near the park-and-ride point. From the perspective of profit maximization and social cost minimization， the optimal parameter setting under two objectives is discussed. The results show that the optimal parameters such as optimal park-and-ride point location and optimal price for a parking space under two objectives all can be obtained where the supply of shared parking spaces is just equal to its demand. It can provide theoretical reference and decision-making basis for optimal allocation of resources in the era of autonomous driving in this paper.

IIn the practice of China Railway High-speed（CRH） Express， the uncertainty of supply and demand and the unreasonable setting of freight nodes greatly increase the total cost of CRH Express transportation. In this paper， the economy of hub construction and transformation and the operation of the existing high-speed rail network are comprehensively considered. Within the framework of a hub-and-spoke network， a distributionally robust optimization approach is employed to account for demand uncertainty. Furthermore， the optimization model is formulated as a mixed-integer second-order cone programming problem. Because of the low efficiency of solving the nonlinear mixed integer programming directly， the outer approximation （OA） algorithm is introduced. Numerical experiment results show that OA algorithm can effectively improve the solving efficiency. The simulation results demonstrate that the distriutionally robust model achieves a more stable network system， improving both the reliability and operational efficiency of high-speed rail freight network， which provides valuable managerial insights for the hub location planning in the CRH Express network.

With the rapid advancement and widespread adoption of E-commerce， online shopping has become an integral component of modern life. However， the unique characteristics of online orders pose significant challenges to warehouse operational efficiency. A primary concern is the heightened consumer demand for timely deliveries， particularly with the increasing prevalence of next-day and same-day delivery services， which substantially amplify delivery complexities. Additionally， the sheer volume of daily orders， each typically comprising multiple stock-keeping units （SKUs）， coupled with the overlap of SKUs across different orders， further complicates the process. Moreover， real-time orders often originate from diverse geographic regions， introducing additional logistical challenges. These factors collectively exert considerable pressure on the order picking and delivery systems of E-commerce enterprises. According to Statista， during 2020–2021， over 4.5% of Amazon orders experienced delays. In traditional picker-to-parts systems， pickers spend approximately 60% of their time traveling within the warehouse. To enhance efficiency and reduce operational costs， E-commerce platforms have increasingly adopted automated order picking systems， such as parts-to-picker systems or robotic mobile fulfillment systems （RMFS）， exemplified by Amazon’s Kiva system and JD.com’s Ground wolf system. Motivated by these operational challenges， this study focuses on optimizing decision-making processes in intelligent warehouses with delivery deadlines to minimize total order tardiness.

Key operational challenges include order assignment， order sequencing， and rack visit sequencing. （i） Order assignment： In warehouses with multiple parallel picking stations， balancing workloads and optimizing efficiency across stations is critical due to variations in order sizes， SKU compositions， and delivery due dates. （ii） Order sequencing： Orders arrive with varying due dates， necessitating prioritization of urgent orders to reduce the likelihood of tardiness. （iii） Rack visit sequencing： Adjacent orders at a picking station may share common SKUs， enabling simultaneous retrieval. However， since each rack carries only one type of SKU， determining the optimal sequence for retrieving SKUs—equivalent to sequencing rack visits—becomes a pivotal issue.

To address these challenges， this study formulates a mixed-integer programming model aimed at minimizing total order tardiness in parts-to-picker systems with delivery due dates. Leveraging the model’s characteristics， an improved knowledge-guided fruit fly optimization algorithm （IKGFOA） is proposed to determine optimal order allocation and sequencing schedules. The algorithm incorporates heuristic rules to accelerate solution convergence and introduces a knowledge-guided search mechanism to balance local and global search capabilities， thereby enhancing the rationality of order assignment and sequencing decisions. Additionally， a shortest waiting time （SWT） rule is designed to optimize order picking and rack visit sequences at each picking station.

The feasibility of the proposed model and the effectiveness of the algorithm are validated through numerical experiments. Small-scale experiments demonstrate that the IKGFOA-SWT algorithm achieves solutions comparable to those obtained by CPLEX under certain conditions. Large-scale experiments further confirm the algorithm’s superiority over the commonly used first-come-first-served （FCFS） strategy in real-world applications. The model and algorithms developed in this study provide E-commerce enterprises with scientifically robust decision-making tools， emphasizing the importance of incorporating delivery due dates into operational optimization strategies to minimize order tardiness in intelligent warehouses.

Due to the uneven distribution of tidal passenger flow in time and space of urban rail transit， the passengers in the direction of large passenger flow are seriously delayed in the station and the train capacity in the direction of small passenger flow is redundant. From the perspective of traffic demand， the minimum number of delayed passengers in the station and the minimum time of passengers in the train is taken as the optimization objectives respectively， and the urban rail transit timetable optimization model considering the skip-stop strategy is constructed and the simulated annealing algorithm is used to solve it. In order to verify the validity of the model and the algorithm， based on the actual operation data of an urban rail transit line， the results show that by using the cooperative optimization strategy of adjusting the train headway and train skip-stop pattern， the total number of delayed passengers in the station is reduced by 7.1%， and the total time of passengers in the train is reduced by 2.2%. Finally， the sensitivity analysis of the number of jumping stations can provide a variety of selection strategies for operators.

The rapid development of artificial intelligence has accelerated the intelligent transformation of warehouses， more and more warehouses have introduced a large number of AGVs to replace manual operations. The online global scheduling problem of multi-AGV in intelligent warehouses is a hot and challenging topic. It integrates task assignment and conflict-free routing， and also needs to consider constraints such as bidirectional network， conflict-free， AGV battery limitation， task time windows， etc. The objective of this problem is to minimize the sum of AGV running time and penalty cost of AGVs waiting at grids. Exploiting the structure of the problem， a mixed-integer linear programming model is established first. Meanwhile， a multi-AGV task assignment and path planning collaborative optimization algorithm is designed： firstly， an assignment algorithm is designed based on the dual priority rules of task and AGV. Secondly， considering multiple collision situations， five strategies are introduced to improve the A* algorithm and a rescheduling mechanism is set to globally plan the AGV path. To verify the performance of our algorithm， a branch-and-cut algorithm is introduced to solve small-scale problems. The results of 12 small-scale instances show that the branch-and-cut algorithm can improve the lower bound of CPLEX by 59.89% on average. The average gap between the results of our heuristic algorithm and the lower bound of the branch-and-cut algorithm is 7.23%. The results of 96 large-scale instances show that all five strategies are valid. Compared to traditional algorithms， the results are improved by our algorithm by an average of 27.69%， the solution time is shortened to within 2s， and the solution efficiency is improved by 130.01% on average. The research is not only applicable to the scheduling decision of AGVs in intelligent warehouses， but also can be extended to closed scenarios with relatively regular networks and high automation degrees， such as production workshops and automated docks， providing a reference for multi-vehicle scheduling problems with centralized global control.

With the rapid development of the online freight platforms， they provide two transport modes as a new solution for manufacturers’ logistics outsourcing， namely “Uploading the Source of Goods” and “Finding Trucks to Transport Goods”. During the transportation of goods， a higher logistics service effort level can reduce waste of resource and improve transport efficiency. Considering logistics service effort， a tripartite game model of manufacturer， online freight platform， and retailer is described to study their decisions and pricing strategies. By solving the model， equilibrium outcomes are characterized， including effort level， prices， profits， consumer surplus， and social welfare. Meanwhile compareing the equilibrium outcomes under several cases to investigate the impact of the logistic service effort. Moreover comparing the cases with and without logistic service effort， it is found that when considering the logistics service effort， the goods’ selling price， wholesale price， and transportation price will decrease， in which case consumer surplus and social welfare will increase. The findings also indicate that making logistics service effort cannot always improve the profits of the participants. The sharing ratio of logistics service effort cost has an important impact on the mode choice of the online freight platform and the manufacturer. It is found that in the mode “Uploading the Source of Goods”， the online freight platform is more beneficial when it pays a smaller proportion of the cost of logistics service effort； while in the mode “Finding Trucks to Transport Goods”， the manufacturer is more beneficial when it pays a smaller proportion of the cost of logistics service effort. The interval of cost-sharing proportion for the win-win situation between the manufacturer and the online freight platform is shown under different transport modes. Furthermore， the existence of the logistics service effort tends to benefit the retailer all the time， and it is more likely to increase the online freight platform’s profit than the manufacturer’s profit.

In recent years， many large e-commerce platforms provide consumer credit services for customers， but there are few studies on consumer credit strategies. Aiming at the secondary supply chain composed of a manufacturer and an e-commerce platform， considering that a bank provides credit card service to some customers， the consumer credit service strategy of the e-commerce platform under wholesale mode and agency mode and the conditions for launching consumer credit service are studied based on game theory. It is found that whether in wholesale mode or agency mode， the launch of consumer credit on the platform will increase the retail price of the product and expand the market demand at the same time. In agency mode， when bad debt risk of consumer credit and platform commission rate are low， the platform will launch a consumer credit service. At this time， platform credit service and bank credit card service are not exactly a competitive relationship. The platform， the manufacturer， and the bank can achieve a win-win-win situation， but the bank needs to take measures to prevent consumers from switching to platform consumer credit service. In wholesale mode， when bad debt risk is low， the platform provides consumer credit service. At this time， the profit of the bank will be reduced， and the impact on the profit of the manufacturer is related to bad debt risk. When bad debt risk is extremely low， the manufacturer and the platform can achieve win-win cooperation. In both modes， bad debt risk does not affect the retail price but has an important impact on whether the platform will launch consumer credit， which is consistent with the fact that e-commerce platforms conduct risk prediction and control through big data， cloud computing， and other information technologies based on their huge user data. A theoretical basis is provided for the implementation of credit services on e-commerce platforms and also puts forward management implications. It is suggested that e-commerce platforms should focus on controlling risks and expanding service layout in the future.

With the increasing shortage of resources and environmental pollution， most manufacturers have started to recycle used products in order to reduce production costs and increase capacity efficiency. However， the manufacturer and the recycler usually act as different stakeholders in the recycling process， and the recyclers’ private information about their capacity endowment and unobservable effort behaviors affects the allocation efficiency of reverse supply chain. with the development of today's network economy and information technology， the recycling channels have been broadened. The manufacturer often outsources recycling to multiple recyclers， while there is also extensive competition or cooperation relationship between the recyclers. In this context， the following three questions are studied：（1） How can the manufacturer design effective incentive mechanism to screen the true information about the recyclers’ capacity and motivate effort behavior？ （2） What are the characteristics of the manufacturer's optimal incentive contract under the different recovery mode？ （3） How do the manufacturer’s recovery mode decisions depend on factors such as the intensity of competition or cooperation and the recyclers' capacity endowment？ In this paper， a two-channel reverse supply chain consisting of one manufacturer （the principal） and two recyclers （two agents） is considered， and the incentive model which can overcome adverse selection and moral hazard simultaneously under different recovery mode are constructed. In the model， based on the revelation principle， a menu of blanket contracts which can identify the true types of the recyclers and drive the optimal effort is provided. The main work includes three parts.Firstly， the feasible allocation sets composed of recovery performance-payment and the incentive compatibility constraints motivating the recyclers to display true information and paying the best effort are constructed. Secondly， the optimal contract structure under symmetric and asymmetric information about the recyclers’ capacity is determined， and the optimal expected utility levels of the manufacturer and the recyclers under different recovery mode are obtained. Thirdly， the characteristics of the optimal incentive contracts including the performance sharing ratio， the recyclers’effort levels and the information rents are analyzed， and the boundary conditions for the selection of recovery mode are identified. The main results are as follows： （1） The recovery performance sharing ratio increases with the cooperation intensity coefficient， and first increases and then decreases as the competition intensity coefficient increases under certain conditions. （2） The recyclers’ effort levels decrease with the effort cost coefficient， absolute risk aversion coefficient and market uncertainty， and increase with the competition or cooperation intensity coefficient and the effort output coefficient. （3） Compared with the case of symmetric capacity information， the recovery performance sharing ratio and effort levels of the recyclers （except the highest capable one） are distorted downward under the two types of information asymmetry， and the recyclers （except the lowest capable one） obtain additional information rents. （4） With the two types of asymmetric information， under certain conditions， the manufacturer would prefer to choose more competent recycler to establish cooperative recycling channel since it is more conducive to ensuring recycling efficiency and economic benefits. The results of this paper provide theory and methodology for the mechanism design with competitive and cooperative relationships between the recycling channels under asymmetric information.

With the rapid development of E-commerce， several farmers have opened online sales channel and shifted to a sale model of agricultural products through both online and offline. Fresh agricultural products need low temperature storage after harvest because of their perishable characteristic. Small-scale farmers in China often seek third-party cold storage services. However， because of the small scale and scattered production of farmers， their cold storage services put forward new requirements of convenience and availability. Conventional cold storage operators mostly serve enterprises， wholesalers and cooperatives. Meanwhile， the existing layout of cold storage is hard to meet the specific requirements of small-scale farmers. Therefore， how to choose the locations of cold storages and determine the service relationship of each storage to farmers become a key issue for many cold storage operators. In view of the location problem， many previous models mainly pursued to minimize costs while ignoring customers' satisfaction. Combined with the characteristics of small and dispersed cold storage demand of E-commerce farmers， farmers' satisfaction is measured from two dimensions： reliability of cold storage service and freshness of fresh products in transportation. Motivated by these observations， in the work a mixed integer linear programming model is proposed， considering factors such as distance， demand， service radius， fixed cost， variable cost， transportation cost and farmers' satisfaction. The farmers' satisfaction is introduced into the model in the form of penalty cost. The objective of the model is to minimize the total cost by optimizing the location of each cold storage and identifying the service relationship between cold storage and the farmers. In addition， an improved artificial bee colony algorithm is proposed， where the chaotic mapping is introduced in the initialization and scout bee search stages to improve the speed of the algorithm. The effectiveness and advantage of the improved artificial bee colony algorithm （IABC） are demonstrated by practical and random examples. Compared with the artificial bee colony algorithm （ABC） and particle swarm optimization algorithm （PSO）， the average solution time of IABC is reduced by 10.77% and 51.6% respectively. In the case study， the example data of apple cold storage locations in Luochuan County are taken as an example to verify the effectiveness of the work， and the influences of demand size， transportation distance and farmer satisfaction penalty cost on total cost， location results and service relationship are explored. Experimental results show that the total cost of cold storage is optimal when constructing 7 cold storages in the case of Luochuan County. The farmer's satisfaction has an important impact on the location of cold storage. Moreover， when the demand and transportation distance increase， increasing the number of cold storage is more advantageous than increasing the capacity of cold storage， which can effectively assist cold storage operators to make location decisions. Reasonable cold storage location decisions can effectively reduce postharvest losses of fresh agricultural products and have important significance to entire supply chain. Considering cold storage reliability and freshness of transportation services for farmers' satisfaction provides a new method of measuring the cold storage service.

The electricity sector is responsible for a major share of carbon dioxide emissions， to attach the carbon peaking and carbon neutrality goals， it is much significant to make renewable energy generation and consumption more effective and efficient. Currently， the consumption of clean electricity needs to be focused， on which there is much renewable power has been curtailed in China， a main reason being the imbalance of renewable power generation and consumption between the generators and electricity retailers. Meanwhile， there is a big gap in the government’s subsidy on renewable power generation， and the mechanism for market regulation performs insufficiently. As the regulations from the government in China， if generators trade renewable power or green certificates corresponding to renewable electricity in the electricity market or tradable green certificate market， the government needs not to offer subsidies to this part of the electricity generated from renewable sources， which is beneficial to alleviate the subsidy pressure of the government， as long as this part of renewable power or green certificates can be consumed in time by power retailers and power consumers. Moreover， if the renewable electricity quantity which the power retailer consumed， is more than the incentive consumption responsibility weight required， the power retailer can trade excess part， which is called excessive consumption in this paper， in the market.Firstly， the electricity market， tradable green certificate market， and carbon quota trading market are considered as an integrated energy trading platform， which couples carbon emissions with electricity， links generators and retailers. It is assumed that the energy platform supply chain in this paper， consists of a generator， an integrated platform， and a power retailer. Three scenarios are considered， including green electricity–green certificate separation， half-separation， and unity， to construct the optimal incentive contract of consumption for the platform. It is explored that the generator’s maximum effort to green power generation and the electricity retailer’s optimal effort to consume. Then， the optimal contract parameters in three scenarios are compared， with data from the electricity trading platform， green certificate trading platform， and carbon trading platform in China， it is investigated that the optimal prices of on-grid green power and tradable green certificate for renewable power being totally consumed. Also， it is analyzed the impacts of carbon emissions allowance， carbon price， and incentive consumption responsibility weight， on renewable power generation and consumption， and on the revenue of players in this platform supply chain. The results show that： （1） There is an equilibrium contract in various conditions， which incentivizes the generator and retailer to generate and consume green electricity as much as possible when their incentive levels got from the platform satisfy a certain constraint， and it is beneficial for the government to narrow the gap of subsidy when optimizing the prices of green electricity and thermoelectricity. （2） Under information asymmetry， the platform needs to pay extra information rents to induce the generator and power retailer to maximize efforts to generate and consume clean power， same as in information symmetry. （3） The price of the tradable green certificate can regulate the uptake of renewable electricity， and the totally consumption on this supply chain can be achieved when the price of the certificate attaches a certain threshold. （4） The generator’s fixed revenue， which got from the platform， is negatively affected by the carbon emissions quota delivered to it， while the fixed revenue of the electricity retailer is positively influenced by the incentive weight of renewable power consumption.With the incentive contracts of the energy trading platform， generators and power retailers can be incentivized to generate and consume more renewable power， which is beneficial to facilitate renewable power subsidies of the government faded out， carbon emissions reduced， and a clean transition of the power sector.

As the world’s largest carbon emitter and second largest economy， China has pledged that its carbon emissions will peak before 2030. Meanwhile， the intensity of carbon emissions will drop by 60%~65% compared to that of 2005. However， due to the varying carbon emissions trajectories of individual provinces， their carbon emissions paths cannot be one-size-fits-all. Three scenarios， i.e.， benchmark， green development， and high-carbon， are designed based on the “14th Five-Year Plan” of each province. Based on LSTM， the carbon emissions of each province from 2020—2040 are predicted under different scenarios. Finally， the appropriate peak paths for individual provinces are analyzed based simultaneously on carbon emission intensity， cumulative carbon emissions and peak time. The results show that the higher the growths rate is， the later the peak time will arrive； China will achieve its carbon emissions peak before 2030， with a peak level of 11884~11792Mt； 24 provinces can achieve the carbon emissions peak before 2030 under at least one scenario； Beijing， Shanghai， Zhejiang， etc. can achieve negative carbon emissions after 2035； An important reference is provided for national policy makers to allocate emission reduction tasks and optimize emission reduction policies.

To promote the green and low-carbon development of container ports and achieve the carbon peak target， from the perspective of port operators， the combinatorial optimization issue of investments in emission reduction projects of container ports under the carbon peak target is researched at the level of port overall operation planning. Firstly， the measurement scope of carbon emission at container ports is defined. Combined with the existing emission reduction technology of ports， a carbon emission measurement model of container ports based on energy consumption is established and then a calculation method is derived to measure effects of implementing emission reduction projects. Furthermore， a combinatorial optimization model of investments in emission reduction projects of container ports under the carbon peak target is presented with the objective of the minimum carbon emission in a planning year and the variables of the number of emission reduction projects planned to be implemented and the number of equipment updates. Finally， the optimal combinatorial strategy of investments in emission reduction projects is obtained through a case study of a container terminal and numerical analyses are made to obtain several management insights： （1） Influences of the planned annual investment amount on emission reduction are discussed. By presenting the concept of cost performance， the results are conducted an in-depth analysis and this method can provide theoretical support for port managers to determine the investment scheme with the highest cost performance. （2） The impact of container throughput on emission reduction are analysed. It is found that when the port can meet the operation demand corresponding to the change of container throughput， its managers can formulate the same optimal emission reduction project investment strategy， that is， the optimal strategy. In summary， it can provide port managers with the optimal combinatorial strategy of investments in emission reduction projects and has important application value for container ports to achieve the carbon peak target.

Against the backdrop of increasingly severe global climate change， actively and steadily promoting the realization of the "dual-carbon" goal has become an inevitable requirement for China to implement the new development concept and promote high-quality development. The data from 279 prefecture-level cities spanning from 2006 to 2022 are employed， an endogenous growth theoretical model incorporating green finance policies is constructed， and a difference-in-differences model is utilized to empirically examine the carbon emission reduction effects and their influencing mechanisms of the pilot policies in green finance reform and innovation zones.It is found that the establishment of green finance reform and innovation zones has led to a 46.72% reduction in carbon emission intensity at the prefecture-level city level. Mechanism test results indicate that this policy can successfully achieve carbon emission reduction effects by promoting green technological innovation and optimizing industrial structure. Moreover， under the regulatory role of environmental regulations， the carbon emission reduction effects of the policies in green finance reform and innovation zones remain significantly positive. Further analysis reveals that the carbon emission reduction effects are more pronounced in large and above cities and non-resource-based cities. It is demonstrated that the green finance reform and innovation pilot zone policy is an effective measure to achieve the "dual-carbon" goal， and at the same time provides a policy basis and empirical insights for promoting green development and realizing Chinese-style modernization.