Risk-averse Decision Research Articles

An assessment of female executives’ influence on bank performance in Indonesia based on critical mass theory

This study examines how female executives affect bank performance in Indonesia’s emerging market. It also investigates whether a critical mass of females on the board of management impacts bank performance. The sample was obtained from 29 banks, which covers 64.5% of publicly listed banks in Indonesia, for the observation period of 2010–2019. This study employs balanced panel data regression analysis, including the year fixed effect. Five surrogate indicators were used for female executives: female Chief Executive Officer (CEO), female Chief Financial Officer (CFO), the presence of females on the board of management, the proportion of female members on the board of management, and the number of female members on the board of management. Critical mass is reached if there are three or more female members on the board of management. The findings suggest that female executives do not significantly impact bank performance. The critical mass suggests a similar result. The findings are consistent and robust for the additional analysis using a lagged independent variable. Nevertheless, the results show that female CEOs positively impact return on assets (ROA) and return on equity (ROE). Empirical findings in Indonesia suggest that female executives do not affect bank performance. The absence of this effect is likely due to unique aspects of Indonesian culture and the structural ownership of firms. However, female CEOs were shown to improve ROE. The findings imply that females are more risk-averse decision makers than males and tend to choose lower-risk investments, which can improve ROA and ROE.

An IoT-enabled hierarchical decentralized framework for multi-energy microgrids market management in the presence of smart prosumers using a deep learning-based forecaster

The integrated exploitation of different energy infrastructures in the form of multi-energy systems (MESs) and the transformation of traditional prosumers into smart prosumers are two effective pathways to achieve net-zero emission energy systems in the near future. Managing different energy markets is one of the biggest challenges for the operators of MESs, since different carriers are traded in them simultaneously. Hence, this paper presents a hierarchical decentralized framework for the simultaneous management of electricity, heat and hydrogen markets among multi-energy microgrids (MEMGs) integrated with smart prosumers. The market strategy of MEMGs is deployed using a hierarchical framework and considering the programs requested by smart prosumers. A deep learning-based forecaster is utilized to predict uncertain parameters while a risk-averse information gap decision theory (IGDT)-based strategy controls the scheduling risk. A new prediction-based mechanism for designing dynamic demand response (DR) schemes compatible with smart prosumers’ behavior is introduced, and the results illustrate that this mechanism reduces the electricity and heat clearing prices in peak hours by 17.5% and 8.78%, respectively. Moreover, the results reveal that the introduced structure for hydrogen exchange through the transportation system has the ability to be implemented in competitive markets. Overall, the simulation results confirm that the proposed hierarchical model is able to optimally manage the competitive markets of electricity, heat and hydrogen by taking advantage of the potential of smart prosumers.

Probability or time: Effect of presentation format on continuous risky decisions

Abstract Continuous risky decisions refer to decisions that involve trade-offs among options with persistent risks. People can use the probability of occurrence per unit time (e.g., ‘the probability of occurrence is 1% per month’) or the average time of risk occurrence (e.g., ‘the average occurrence time is 100 months’) to represent continuous risky options. In this study, we examined the effect of the presentation format (i.e., the probability of occurrence per unit time vs. the average time of risk occurrence) on continuous risky decisions in the gain domain and further explored the underlying mechanism. In Study 1 (N = 122), we demonstrated the effect of presentation format on continuous risky decisions and the moderating effect of the magnitude of probabilities. Specifically, when the probabilities were relatively low, compared with the probability of occurrence per unit time, using the average time of risk occurrence to present the continuous risky options led to more risk-averse decisions. However, when the probabilities were relatively high, compared with the probability of occurrence per unit time, the presentation format of the average time occurrence led to more risk-seeking decisions. In Study 2 (N = 136), we found that the moderating effect of the option probabilities on continuous risky decisions was mediated by the subjective attribute-wise difference judgment. In Study 3 (N = 221), we replicated the effect of presentation format on continuous risky decisions in more natural scenarios. The study offered a deep understanding of the mechanism of continuous risky decision-making, and the results were conducive to further developing theories in relevant fields.

Modelling decisions concerning the dispatch of airtankers for initial attack on forest fires in Ontario, Canada

Airtankers are commonly used for initial attack (IA) to reduce the likelihood of wildland fires escaping containment efforts. We examined IA airtanker dispatch decisions for forest fires in Ontario, Canada, through an analysis of historical fire records from 2001 to 2019. A hurdle modelling approach that predicts the probability of airtanker(s) being dispatched and then the number of airtankers sent was used. Two different hurdle models were considered depending on the timing of the information available to the decision maker: a “fire report model” based upon the information available when a fire is first reported, and an “initial attack model” using information available at the time IA action began. Both models indicated that the most influential covariates for airtanker dispatch are the fire weather index, fuel volatility, observed fire rate of spread, fire size at IA, and cause of ignition. When evaluating the predictive ability of the models on a validation data set, the “initial attack model” performed better than the “fire report model”. Our models generally perform well when predicting none, one, or two airtankers on IA, but they generally underpredict when more than two airtankers are dispatched, which suggests risk-averse decision-making in fire management resource dispatching.

Green Supply Chain Decision and Management under Manufacturer’s Fairness Concern and Risk Aversion

This study focuses on the impact of a supply chain manufacturer’s fairness concern and risk aversion on the green supply chain and constructs a two-level green supply chain consisting of a manufacturer and a retailer. It compares three models: the manufacturer is a risk-neutral and fair-neutral, the manufacturer is a risk-averse and fair-neutral, and the manufacturer is a risk-averse and fair-concerned decision maker. In three cases, this paper examines how the manufacturer’s risk aversion and fairness concerns the impact product green level, retail price, and the wholesale price in the green supply chain. Consumers are sensitive to pricing strategies and product green level. The results are as follows: the manufacturer’s risk aversion leads to an increase in the retailer’s profit, and the manufacturer’s profit decreases with the increase in the risk aversion coefficient. Second, when demand meets certain conditions and the manufacturer has advantageous fairness concerns, the manufacturer benefits from fairness concerns, the retailer’s profit decreases with the manufacturer’s fairness concerns coefficient, and the manufacturer’s risk aversion will lead to a decline in the product green level. Third, when the manufacturer believes that the risk is relatively large, they reduce the technology investment of green products; thus, the fairness concerns of the manufacturer will affect the investment of green products and increase the green level of products. For enterprises: When supply chain members face various risks, they need to consider the specific needs of consumers and other members’ behavioral preferences. They can put forward a more scientific prediction of market demand and rational decision making. For customers: they should not only pay attention to changes in market prices, but should also pay attention to changes in the behavioral preferences of supply chain members, as customers are in line with their own interests based on the choice of high-quality green production.

Robust ocean zoning for conservation, fishery and marine renewable energy with co-location strategy

With the increasing human-dominated seascape, maritime exploitation like fishery and marine renewable energy (MRE) is soaring in both abundance and intensity, which brings forward the ocean zoning problem to balance conservation, fishery, and MRE. What complicates the ocean zoning problem is the ambiguity of values (conservation, fishery, and MRE) and opportunity cost due to the scarce data and the complexity of the eco-economy system. To address the ocean zoning problem with ambiguity, we characterize these ambiguous values/cost as ambiguity sets containing all possible distributions from the robust optimization perspective and formulate the values of conservation, fishery, and MRE as three objectives in the setting of risk-averse decision-makers. Furthermore, we adopt the co-location strategy of fishery and MRE as constraints and assign a higher priority to the conservation objective. After deriving and solving an ambiguous multi-objective risk-averse ocean zoning model, we perform the model analysis of the conflict, ambiguity, and Pareto optimality. A case study of the Huanghai sea under the jurisdiction of the West Coast of Qingdao justifies several significant insights. First, the MRE is fully willing to co-locate with the fishery under the pressure of economy and ocean use intensity. Second, the robust strategies incorporating the decision-dependent expectation and lower covariance can ensure the objectivity and rationality of ocean zoning for conservation, fishery, and MRE. Third, the risk-averse degree from ambiguous value plays a pivotal role in the fulfillment of aspiration levels and the zoning strategies. Overall, our method can improve the space allocation performance and provide a coordinated-development strategy for conservation and development.

Risk-Averse Optimal Control in Continuous Time by Nesting Risk Measures

This paper extends dynamic control problems from a risk-neutral to a risk-averse setting. We establish a limit for consecutive risk-averse decision making by consistently and adequately nesting coherent risk measures. This approach provides a new perspective on multistage optimal control problems in continuous time. For the limiting case, we elaborate a new dynamic programming principle, which is risk averse, and give risk-averse Hamilton–Jacobi–Bellman equations by generalizing the infinitesimal generator. In doing so, we provide a constructive explanation of the driver g in g-expectation, a dynamic risk measure based on backward stochastic differential equations. Funding: This work was supported by Deutsche Forschungsgemeinschaft [Project-ID 416228727 – SFB 1410].

Risk-averse rehabilitation decision framework for roadside slopes vulnerable to rainfall-induced geohazards

Rainfall-induced slope failures disrupt the traffic and warrant urgent slope repair works. The impact of roadside slope failures can be minimized if slopes are proactively rehabilitated. Nonetheless, transportation agencies are constrained in their budget to rehabilitate a limited number of slope segments due to competing maintenance needs among different transportation assets. Therefore, the transportation agencies should identify the critical slope combination that should be proactively rehabilitated under constraint budgets to lessen the impact on the transportation network during extreme rainfall events. The decision-making approach for slope rehabilitation should also ensure low risk associated with the selected rehabilitation strategy. Current slope-rehabilitation decision models do not consider the risk associated with the rehabilitation strategies in the decision-making process. The objective of this study is to develop a risk-averse stochastic combinatorial optimization to facilitate the selection of slope rehabilitation strategies, which leads to the least expected cost and conditional value at risk (CVaR) for extreme rainfall events. The simulated annealing approach is used to solve the risk-averse combinatorial optimization rehabilitation problem with the objective function that measures the total cost of traffic disruption and slope restoration post-failures. The approach is demonstrated using a transportation network in Lamar County, Texas. Unlike a genetic algorithm-based approach in the literature that yields a single slope rehabilitation strategy, the proposed risk-averse simulated annealing approach identifies rehabilitation strategies along the Pareto efficient frontier facilitating the rehabilitation decisions based on the tradeoff between expected cost and CVaR. For the network in Lamar County, the proposed risk-averse simulated annealing provided a solution in the Pareto front that reduced CVaR by 2.0% compared to the solution obtained from the genetic algorithm-based approach while only increasing the expected cost by 0.8%. The risk-averse optimization approach will aid transportation agencies in determining slope rehabilitation strategies for minimizing the impact of rainfall-induced failures at appropriate risk aversion levels.

Risk-standardized mortality metric to monitor hospital performance for chest pain presentations.

Risk-standardized mortality rates (RSMR) have been used to monitor hospital performance in procedural and disease-based registries, but limitations include the potential to promote risk-averse clinician decisions and a lack of assessment of the whole patient journey. We aimed to determine whether it is feasible to use RSMR at the symptom-level to monitor hospital performance using routinely collected, linked, clinical and administrative data of chest pain presentations. We included 192 978 consecutive adult patients (mean age 62 years; 51% female) with acute chest pain without ST-elevation brought via emergency medical services (EMS) to 53 emergency departments in Victoria, Australia (1/1/2015-30/6/2019). From 32 candidate variables, a risk-adjusted logistic regression model for 30-day mortality (C-statistic 0.899) was developed, with excellent calibration in the full cohort and with optimism-adjusted bootstrap internal validation. Annual 30-day RSMR was calculated by dividing each hospital's observed mortality by the expected mortality rate and multiplying it by the annual mean 30-day mortality rate. Hospital performance according to annual 30-day RSMR was lower for outer regional or remote locations and at hospitals without revascularisation capabilities. Hospital rates of angiography or transfer for patients diagnosed with non-ST elevation myocardial infarction (NSTEMI) correlated with annual 30-day RSMR, but no correlations were observed with other existing key performance indicators. Annual hospital 30-day RSMR can be feasibly calculated at the symptom-level using routinely collected, linked clinical, and administrative data. This outcome-based metric appears to provide additional information for monitoring hospital performance in comparison with existing process of care key performance measures.

Inventory control under corporate income tax and accrual accounting

Corporate income tax constitutes a significant portion of financial costs in business operations. A typical tax system is characterized by the tax structure, accounting method, and loss carryover rule. In this article, we study the optimal inventory policies under taxation and accrual accounting. We consider a lost-sales periodic-review inventory system under a proportional tax with loss carryforward. We formulate the problem as a cyclic stochastic dynamic program with multiple accounting periods, each of which consists of multiple review periods. We show that an income-dependent basestock policy is optimal. We find that tax function convexity and loss carryforward may introduce conflicting incentives into inventory decisions: the former drives risk-averse decisions whereas the latter induces risk-seeking behavior. We identify two intertemporal effects of taxation, the intra-accounting-period effect and the inter-accounting-period effect, both inducing smaller basestock levels. We further extend the analysis to the progressive tax, backordering system, and cash accounting method. Our numerical study examines the effects of various characteristics of taxation on the optimal policies. Our results demonstrate that ignoring taxation in inventory decisions may lead to significant losses especially when the tax rate and demand uncertainty are sufficiently high, which reveals the value of tax considerations in inventory management.

Could the Incentive Policy of Shared Parking Be Sustainable? The Viewpoint of Personal Risk Aversion

Using an incentive measure to encourage people to share their private parking spaces could be an effective strategy for urban parking problems. This paper discusses an innovative mechanism of shared parking, “FlexPass,” which applies a reverse auction in which drivers propose bids in line with their individual expectations to share their idle parking spaces. The auction mechanism, hypotheses on bidding process principles, the competitive environment, and the risk-averse decisions of providers with regard to parking spaces are analysed to explore the sustainability of the economic benefits obtained for FlexPass parking spaces. A total of 216 respondents from the University of California, Berkeley, were invited to participate in bidding in an actual survey during their daily use of parking spaces. The analytical results show that operational rules based on risk aversion can enable profit-seeking with a bounded capability to obtain considerable economic benefits and release parking resources in an environment of demand competition. Particularly in some scenarios, FlexPass would sacrifice a certain monetary income to ensure the perceived benefits of parking space providers. With the improvement of people’s enthusiasm for participating in shared parking, the benefits to individuals and parking lots would be further enhanced, suggesting that our mechanism can operate sustainably over the long term. These findings are helpful for policymakers to formulate feasible shared parking policies from the perspective of monetary incentives.

313.15: Cost-Effectiveness of Interventions to Increase Utilization of Kidneys From Deceased Donors With Primary Brain Malignancy in an Australian Setting

Introduction: Kidneys from potential deceased donors with brain cancer are often foregone due to concerns of transmission risk to recipients. This may be due to uncertainty around donors’ medical history and their absolute transmission risk, or risk-averse decision-making among clinicians. However, transmissions are rare and prolonging waiting time for recipients is harmful. Method: We assessed the cost-effectiveness of increasing utilization of these potential donors using a Markov model patient simulation (Figure 1) to estimate costs and consequences from a payer perspective (Australian government) using linked transplant registry data. We estimated costs and quality-adjusted life-years (QALYs) from three interventions: decision support for clinicians in assessing donor risk, improved data accuracy with real-time data-linkage to hospital records and cancer registries, and increased risk-tolerance to allow intermediate-risk donors. Results: Decision support increased donation 0.3% with a 2% transmission rate and was dominant (improved QALYs and cost-saving) in 56% of simulations (Figure 2). Real-time data-linkage increased donation 0.6% with 1.8% transmissions and was dominant in 57% of simulations. Increasing risk tolerance increased donation by 2.1% with 3.3% transmissions and was dominant in 75% of simulations (mean +10.0 QALYs and $1.2m ($904k USD) cost-savings). Conclusion: Accepting intermediate-risk donors with brain cancer is likely to improve patient outcomes and reduce overall healthcare expenditure.This work was funded by a National Health and Medical Research Council Postgraduate Research Scholarship (APP1191066).

Modeling total predation to avoid perverse outcomes from cat control in a data-poor island ecosystem.

Data hungry, complex ecosystem models are often used to predict the consequences of threatened species management, including perverse outcomes. Unfortunately, this approach is impractical in many systems, which have insufficient data to parameterize ecosystem interactions or reliably calibrate or validate such models. Here we demonstrate a different approach, using a minimum realistic model to guide decisions in data- and resource-scarce systems. We illustrate our approach with a case-study in an invaded ecosystem from Christmas Island, Australia, where there are concerns that cat eradication to protect native species, including the red-tailed tropicbird, could release meso-predation by invasive rats. We use biophysical constraints (metabolic demand) and observable parameters (e.g. prey preferences) to assess the combined cat and rat abundances which would threaten the tropicbird population. We find that the population of tropicbirds cannot be sustained if predated by 1607 rats (95% credible interval (CI) [103, 5910]) in the absence of cats, or 21 cats (95% CI [2, 82]) in the absence of rats. For every cat removed from the island, the bird's net population growth rate improves, provided that the rats do not increase by more than 77 individuals (95% CI [30, 174]). Thus, in this context, one cat is equivalent to 30-174 rats. Our methods are especially useful for on-the-ground predator control in the absence of knowledge of predator-predator interactions, to assess whether 1) the current abundance of predators threatens the prey population of interest, 2) managing one predator species alone is sufficient to protect the prey species given potential release of another predator, and 3) control of multiple predator species is needed to meet the conservation goal. Our approach demonstrates how to use limited information for maximum value in data-poor systems, by shifting the focus from predicting future trajectories, to identifying conditions which threaten the conservation goal. This article is protected by copyright. All rights reserved.

Optimizing a condition-based maintenance policy by taking the preferences of a risk-averse decision maker into account

Reasonably accurate remaining-useful-life (RUL) predictions allow for the introduction of maintenance policies where resources, such as spare parts and personnel, are only acquired based on the predicted need. For some assets, such a policy will help reduce the cost of renewals but will also increase the probability of renewal cycles with long downtime and associated large losses. From a decision theoretical point of view decision makers are often risk-averse and therefore their financial risk tolerance should be considered. This paper presents a procedure based on expected utility theory for the optimization challenge. To calculate the expected utility the characteristic function is used to find the full probability mass function of the maintenance cost in a finite time interval. A numerical example and a case study, based on data from an offshore oil and gas platform, are presented to illustrate the proposed model. These examples show that using the long-run cost rate to optimize the presented maintenance policy may lead to decisions that are not in line with the preferences of a risk-averse decision maker.

Dual-Source Procurement Strategy of Cross-Border E-Commerce Supply Chain considering Members’ Risk Attitude

The risk attitude of decision-makers will significantly affect the decision-making of enterprise risk management. Specifically, high risk represents the potential premise of high return for risk preference decision-makers, and for risk-averse decision-makers, the increase of risk degree will stimulate decision-makers’ aversion to uncertainty and turn to seek safer business strategies. Although there are many pieces of literature on the risk preference of decision-makers, they usually only assume the risk attitude of one party and rarely consider the risk attitude of suppliers and retailers in the scenario of cross-border e-commerce at the same time. Therefore, under the background of supply disruption, for the cross-border e-commerce supply chain composed of cross-border suppliers, overseas suppliers, overseas retailers, and consumers, combined with the risk attitude preference of enterprise subjects, this paper constructs the mean-variance model dominated by overseas retailers and reversely solves the risk-aversion attitude of a single cross-border supplier. When a single overseas retailer maintains a risk-aversion attitude and both cross-border suppliers and overseas retailers hold a risk-neutral or risk-aversion attitude, the pricing of products in different channels is analyzed. Finally, an example is given to analyze the impact of supply disruption probability, risk-aversion coefficient, channel distribution coefficient, and other parameters on purchase price, market demand, target profit, and utility. It is of great practical significance for improving the stability of cross-border e-commerce supply chain system and reducing revenue loss to study how different degrees of risk-aversion attitudes of cross-border suppliers and overseas retailers affect enterprise procurement pricing strategy, target profit, and utility in case of supply disruption.

Mixed financing modes for capital-constrained supply chain with risk-averse members

This paper considers a two-echelon supply chain consisting of a supplier and a capital-constrained retailer. Both the supplier and the retailer are risk-averse decision makers. The capital-constrained retailer may adopt two mixed financing modes: (1) bank credit and equity financing (BEF) and (2) trade credit and equity financing (TEF). Using a mean-variance framework, we analyze the supply chain members financing and ordering decisions in two cases: symmetric and asymmetric retailer risk aversion threshold information. In the case of symmetric information, we characterize the conditions under which both the supplier and the retailer prefer BEF or TEF. In the case of asymmetric information, we demonstrate that the retailer has an incentive to pretend to be less risk averse. To prevent this distortion behavior, we design a minimum quantity contract for the supplier. Finally, we extend our model to a bank loan-trade credit-equity mixed financing mode (BTEF) in which the retailer can borrow from the bank and the supplier and seeks financial support from investors. The numerical simulations support our results.

Risk-averse stochastic bilevel programming: An application to natural gas markets

This paper presents a risk-based integer bilevel programming model that represents two players (leader and follower) who interact strategically over an infrastructure network. Both players seek to maximize their individual profits in the face of demand uncertainty. Variations of the basic model are proposed that allow us to identify optimal strategies under risk-neutral and risk-averse decision-making. To solve the resulting stochastic bilevel problem, we develop an algorithm that relies on iteratively solving a restricted version of the problem to obtain feasible solutions to the original model. Extensive computational experiments are performed to evaluate algorithmic tractability and solution quality. The methodology is demonstrated using an example that addresses emerging strategic issues in natural gas markets. The model considers two players, a liquefied natural gas (LNG) operator as the leader and a natural gas (NG) producer as the follower. The LNG operator makes decisions on the locations of LNG facilities and the amount of gas to buy from the producer. The NG producer decides how much gas to produce and how to ship it through the existing pipeline network. Both players face demand uncertainty in their respective markets and have to make investment, production, and sales decisions. Our case study focuses on the Gulf-Southwest region of the United States and demonstrates the impact of risk-averse decision-making on investment and operational decisions.

Robust optimization approaches for purchase planning with supplier selection under lead time uncertainty

Supplier reliability is a critical issue for manufacturing companies. Delivery delays from suppliers create backlog and firefighting on the shop floors. To avoid disruption and hedge against supplier lead time uncertainty, companies rely on diversification, multi-sourcing, and safety lead times. In multi-sourcing, the buyer might order the same product (raw material) from different suppliers. The design of a robust and cost-efficient purchasing/ordering plan in multi-sourcing is a complex task which has a strong impact on the performance of the company. In this study, we investigate the use of robust optimization for the integrated lot-sizing and supplier selection problem under lead time uncertainty. More specifically, we use polyhedral budgeted uncertainty sets. The resulting model determines the ideal lot sizes to minimize the total costs taking into consideration suppliers’ reliability and prices. To solve this problem, a row and column generation approach is proposed. To alleviate scalability issues, we enhance the row and column generation through a robust counterpart formulation, and we propose an efficient fix-and-optimize approach. Our extensive computational experiments show that the fix-and-optimize approach yields good quality solutions within a reasonable amount of computational time. We provide insights into supplier diversification based on the risk profile of the decision-maker. One of the conclusions is that an extremely risk-averse decision-maker selects a single supplier, namely the most reliable one even if it does not offer the lowest price.

Many-objective charging optimization for electric vehicles considering demand response and multi-uncertainties based on Markov chain and information gap decision theory

This paper constructs an electric vehicle (EV) charging optimization model considering demand response and the uncertainties of source and load. First, after obtaining the charging load by using the Markov chain, the charging time is divided into peaks, flats, and valleys, and the charging load is adjusted through price-based demand response. Secondly, aiming at minimizing user charging cost, greenhouse gas (GHG) emissions, and load fluctuation, and maximizing the revenue of power supplier, a deterministic EV charging optimization model is established. Third, by modeling information gap decision theory (IGDT), the uncertainty of wind and photovoltaic powers (WP and PV) is introduced into the charging optimization model to analyze the impact of WP and PV's fluctuations on risk aversion decision makers. Finally, a joint algorithm for solving a many-objective problem based on ε-constraint and NSGA-II algorithm is proposed. The results of case study show that: (1) rolling forecast based on Markov chain has a better effect on dealing with the uncertainty of charging load than Monte Carlo; (2) the price-based demand response combined with time division model can be used to shave peaks and fill valleys and reduce user charging cost; (3) optimized by using the deterministic model, the charging load curve is smoother; (4) with the uncertainty of power generation involved in the many-objective optimization, the PV's fluctuation is greater, and the power supplier revenue increases compared with the deterministic model, but the GHG emissions also increase; (5) In the same range of expected benefit changes, the fluctuation values acceptable to decision makers are different for different objective functions; (6) the user response, the normalized number of the selected sub-objective, and the maximum acceptable benefit deviation have different influences on the ordered charging strategy; (7) the joint algorithm proposed in this paper is an effective method to solve many-objective problems that contain conflictive aims.

Coordination Decisions for a Low-Carbon Supply Chain Considering Risk Aversion under Carbon Quota Policy.

To mitigate climate change, the governments of various countries have formulated and implemented corresponding low-carbon emission reduction policies. Meanwhile, consumers’ awareness of the necessity of environmental protection is gradually improving, and more consumers pay attention to the environmental attributes of products, which all encourages enterprises to have great power to implement low carbon technology. As rational decision makers, members tend to show the characteristics of risk aversion. How to meet the needs of consumers and reduce their own risks has become a key point of low-carbon supply chain management. Considering carbon quota policy, in this paper, the optimal pricing decision-making process of a supply chain system is discussed under risk-neutral and risk-avoidance decision-making scenarios by game theory, and a cost-sharing contract is used to coordinate the decision-making process of a supply chain system. By analyzing the influence of the risk aversion coefficient on the optimal strategies of participants, we find that when the manufacturer has the risk aversion characteristic, the risk aversion coefficient will further reduce the carbon emission rate, the wholesale price of the product and the manufacturer’s profit but increase the product order quantity and the retailer’s profit. In addition, if consumers have a high preference for low-carbon products, the manufacturer’s risk-aversion coefficient will lead to a lower selling price than in the centralized decision-making situation, and the profit of the supply chain system will also be further reduced. When the cost-sharing contract is adopted for coordination, the Pareto improvement of supply chain members’ profits can be achieved when the parameters of the cost-sharing contract are appropriate, regardless of the manufacturer’s risk-neutral decision or risk-aversion decision.