Dynamic decision and its complex dynamics analysis of low-carbon supply chain considering risk-aversion under carbon tax policy

This study establishes a low-carbon supply chain game model under the centralized decision situation and the decentralized decision situation considering the manufacturer risk-aversion behavior, and discusses the influence of the manufacturer risk-aversion behavior on the optimal decision, profit, coordination, and complex dynamics of the supply chain. We find that comparing with the risk-neutral decentralized decision, the increase of manufacturer's risk tolerance attitude can narrow the gap between the supply chain profit and the centralized decision, but it will further reduce the carbon emission reduction level. The increase of risk tolerance of the manufacturer and carbon tax will narrow the stable region of the system. Under this situation, the manufacturer should carefully adjust parameters to prevent the system from losing stability,especially the adjustment parameters for carbon emission reduction level. When the system is in a chaotic state, the increase of carbon tax rate makes the system show more complex dynamic characteristics. Under the chaotic state, it is difficult for the manufacturer to make correct price decision and carbon emission reduction strategy for the next period, which damages its profit, but increases the profit of the retailer and the supply chain. Finally, the carbon emission reduction cost-sharing contract is proposed to improve the carbon emission reduction level and the supply chain efficiency, achieving Pareto improvement. The stability region of the system is larger than that in the centralized decision situation, but the increase of the cost sharing coefficient will reduce the stability of the system in the decentralized decision-making situation.

A Risk-Averse Remaining Useful Life Estimation for Predictive Maintenance

Remaining useful life(RUL)prediction is an advanced technique for system maintenance scheduling.Most of existing RUL prediction methods are only interested in the precision of RUL estimation;the adverse impact of overestimated RUL on maintenance scheduling is not of concern.In this work,an RUL estimation method with risk-averse adaptation is developed which can reduce the over-estimation rate while maintaining a reasonable under-estimation level.The proposed method includes a module of degradation feature selection to obtain crucial features which reflect system degradation trends.Then,the latent structure between the degradation features and the RUL labels is modeled by a support vector regression(SVR)model and a long short-term memory(LSTM)network,respectively.To enhance the prediction robustness and increase its marginal utility,the SVR model and the LSTM model are integrated to generate a hybrid model via three connection parameters.By designing a cost function with penalty mechanism,the three parameters are determined using a modified grey wolf optimization algorithm.In addition,a cost metric is proposed to measure the benefit of such a risk-averse predictive maintenance method.Verification is done using an aero-engine data set from NASA.The results show the feasibility and effectiveness of the proposed RUL estimation method and the predictive maintenance strategy.

The Roles of Female Involvement and Risk Aversion in Open Access Publishing Patterns in Vietnamese Social Sciences and Humanities

Purpose:The open-access(OA)publishing model can help improve researchers’outreach,thanks to its accessibility and visibility to the public.Therefore,the presentation of female researchers can benefit from the O A publishing model.Despite that,little is known about how gender affects OA practices.Thus,the current study explores the effects of female involvement and risk aversion on OA publishing patterns among Vietname se social sciences and humanities.Design/methodology/approach:The study employed Bayesian Mindsponge Framework(BMF)on a dataset of 3,122 Vietnamese social sciences and humanities(SS&H)publications during 2008-2019.The Mindsponge mechanism was specifically used to construct theoretical models,while Bayesian inference was utilized for fitting models.Findings:The result showed a positive association between female participation and OA publishing probability.However,the positive effect of female involvement on OA publishing probability was negated by the high ratio of female researchers in a publication.OA status was negatively associated with the JIF of the journal in which the publication was published,but the relationship was moderated by the involvement of a female researcher(s).The findings suggested that Vietnamese female researchers might be more likely to publish under the OA model in journals with high JIF for avoiding the risk of public criticism.Research limitations:The study could only provide evidence on the association between female involvement and OA publishing probability.However,whether to publish under OA terms is often determined by the first or corresponding authors,but not necessarily gender-based.Practical implications:Systematically coordinated actions are suggested to better support women and promote the OA movement in Vietnam.Originality/value:The findings show the OA publishing patterns of female researchers in Vietnamese SS&H.

Effect of Employees’ Pay-performance Sensitivity on Future Firm Performance: Evidence from State-owned Listed Enterprises in China over 2007-2015

Taking a balanced panel data consisting of 4365 firm-year observations drawn from the listed state-owned enterprises in Shanghai and Shenzhen Stock Exchange over 2007-2015 as the research sample,the paper examines the effect of the employees’pay-performance sensitivity(PPS)on the future firm performance from the two competing perspectives of“incentive effect”and“risk-aversion effect”,adopting the method of multiple regression analysis based on OLS and applying the SPSS23 as the data processing tool.Theoretical analysis and empirical results demonstrate that there is a positive link between the employees’PPS and the future firm performance.To improve the employees’PPS can stimulate the engagement of the employees,improve their working quality,enrich their workplace innovative behavior,and further lead to higher future firm performance.Meanwhile,the positive effect of the employees’PPS on the future firm performance is,on average,lower than that of the top executives’PPS on the future firm performance.Implications of the findings are provided in the end.

Risk-averse Robust Interval Economic Dispatch for Power Systems with Large-scale Wind Power Integration

This paper presents a robust interval economic dispatch(RIED)model for power systems with large-scale wind power integration.Differing from existing interval optimization(IO)approaches that merely rely on the upper and lower boundaries of random variables,the distribution information retained in the historical data is introduced to the IO method in this paper.Based on the available probability distribution function(PDF),wind power curtailment and load shedding are quantified as the operational risk and incorporated into the decision-making process.In this model,we need not rely on the forecasted value of wind power,which is randomly fluctuating and quite unpredictable.Furthermore,when the PDFs of wind power are taken into account,the resulting dispatch solution makes a good tradeoff between the generation cost and the operational risk.Finally,the RIED model yields an optimal dispatch solution for thermal units and the allowable intervals of wind power for the wind farms,which efficiently mitigates the uncertainty in wind power generation and provides more practical suggestions for system operators.Simulation studies are conducted on a modified IEEE-118 bus system and the results verify the effectiveness of the proposed RIED model.

Distributed and Risk-averse ADP Algorithm for Stochastic Economic Dispatch of Power System with Multiple Offshore Wind Farms

With more and more offshore wind power being increasingly connected to power grids,fluctuations in offshore wind speeds result in risks of high operation costs.To mitigate this problem,a risk-averse stochastic economic dispatch(ED)model of power system with multiple offshore wind farms(OWFs)is proposed in this paper.In this model,a novel GlueVaR method is used to measure the tail risk of the probability distribution of operation cost.The weighted sum of the expected operation cost and the GlueVaR is used to reflect the risk of operation cost,which can consider different risk requirements including risk aversion and risk neutrality flexibly by adjusting parameters.Then,a risk-averse approximate dynamic programming(ADP)algorithm is designed for solving the proposed model,in which multi-period ED problem is decoupled into a series of single-period ED problems.Besides,GlueVaR is introduced into the approximate value function training process for risk aversion.Finally,a distributed and risk-averse ADP algorithm is constructed based on the alternating direction method of multipliers,which can further decouple single-period ED between transmission system and multiple OWFs for ensuring information privacy.Case studies on the modified IEEE 39-bus system with an OWF and an actual provincial power system with four OWFs demonstrate correctness and efficiency of the proposed model and algorithm.

Real-time Risk-averse Dispatch of an Integrated Electricity and Natural Gas System via Condi-tional Value-at-risk-based Lookup-table Ap-proximate Dynamic Programming

The real-time risk-averse dispatch problem of an integrated electricity and natural gas system(IEGS)is studied in this paper.It is formulated as a real-time conditional value-at-risk(CVaR)-based risk-averse dis-patch model in the Markov decision process framework.Because of its stochasticity,nonconvexity and nonlinearity,the model is difficult to analyze by traditional algorithms in an acceptable time.To address this non-deterministic polynomial-hard problem,a CVaR-based lookup-table approximate dynamic programming(CVaR-ADP)algo-rithm is proposed,and the risk-averse dispatch problem is decoupled into a series of tractable subproblems.The line pack is used as the state variable to describe the impact of one period’s decision on the future.This facilitates the reduction of load shedding and wind power curtailment.Through the proposed method,real-time decisions can be made according to the current information,while the value functions can be used to overview the whole opti-mization horizon to balance the current cost and future risk loss.Numerical simulations indicate that the pro-posed method can effectively measure and control the risk costs in extreme scenarios.Moreover,the decisions can be made within 10 s,which meets the requirement of the real-time dispatch of an IEGS.Index Terms—Integrated electricity and natural gas system,approximate dynamic programming,real-time dispatch,risk-averse,conditional value-at-risk.

Risk-averse Contextual Multi-armed Bandit Problem with Linear Payoffs

In this paper we consider the contextual multi-armed bandit problem for linear payoffs under a risk-averse criterion.At each round,contexts are revealed for each arm,and the decision maker chooses one arm to pull and receives the corresponding reward.In particular,we consider mean-variance as the risk criterion,and the best arm is the one with the largest mean-variance reward.We apply the Thompson sampling algorithm for the disjoint model,and provide a comprehensive regret analysis for a variant of the proposed algorithm.For T rounds,K actions,and d-dimensional feature vectors,we prove a regret bound of O((1+ρ+1/ρ)d In T ln K/δ√dKT^(1+2∈)ln K/δ1/e)that holds with probability 1-δunder the mean-variance criterion with risk tolerance p,for any 0<ε<1/2,0<δ<1.The empirical performance of our proposed algorithms is demonstrated via a portfolio selection problem.

A Hashing Power Allocation Game with and without Risk-free Asset

Miners in various blockchain-backed cryptocurrency networks compete to maintain the validity of the underlying distributed ledgers to earn the bootstrapped cryptocurrencies.With limited hashing power,each miner needs to decide how to allocate their resource to different cryptocurrencies so as to achieve the best overall payoff.Together all the miners form a hashing power allocation game.We consider two settings of the game,depending on whether each miner can allocate their fund to a risk-free asset or not.We show that this game admits unique pure Nash equilibrium in closed-form for both settings.