Reward-penalty Mechanism Research Articles

Tripartite evolutionary game analysis of governance mechanism in Chinese WEEE recycling industry

The rapidly increase in waste electrical and electronic equipment (WEEE) poses challenges to the government to manage the WEEE recycling industry. In order to better understand and improve the governance mechanism in the WEEE recycling industry, taking China as an example, we construct a tripartite evolutionary game model consisting of the manufacturer, the collector, and the government by underscoring product eco-design and qualified disassembly. First, we obtain the evolutionary stability strategy (ESS) by calculating the replicator dynamic equation and the stability analysis of equilibrium points. Then, we examine the feasibility and rationality of the evolutionary game model by analyzing ESSs corresponding to different stages of the WEEE recycling industry. Finally, we explore the ramifications of the governance mechanism on the behaviors of the manufacturer’s product eco-design and the collector’s qualified disassembly through a numerical example. We find that the evolutionary game model exists eleven equilibrium points and six possible ESSs, and the choice of each ESS depends mainly on the trade-off between costs and revenues for each stakeholder. Through the numerical study, we observe that the current Chinese WEEE governance mechanism is not effective. We also observe that the manufacturer’s willingness to implement product eco-design is decreasing in the tax rate but increasing in the environmental tex, and the collector’s willingness to recycle WEEE through a compliance process is increasing in both the subsidy and the penalty. We suggest that the government focuses on improving the reward-penalty mechanism, establishing appropriate WEEE disposal fund levy and subsidy standards, and removing informal collectors to promote the healthy development of WEEE recycling industry.

The Reward-Penalty Mechanism in a Closed-Loop Supply Chain with Asymmetric Information of the Third-Party Collector

This paper investigates the reward-penalty mechanism (RPM) implemented by the government in a closed-loop supply chain (CLSC) with asymmetric information. The manufacturer produces and sells products to consumers, while the collection of waste electrical and electronic equipment (WEEE) is delegated to the third-party collector, the one who has private information about the collection effort level. An information screening contract for the manufacturer is put forward to obtain the private information from the third-party collector, which is composed of buy-back price and franchise fee. By utilizing principal-agent theory, two cases are mainly examined including the CLSC without the RPM and the CLSC with the RPM. The results demonstrate that (i) the information screening contract is effective in capturing the collector’s collection effort level, (ii) raising the buy-back price to motivate the third-party collector is confirmed to perform well on enhancing the collection quantity from consumers, (iii) H-type collector collects more WEEEs and earns more profits than L-type collector, and (iv) the RPM improves the collection quantity of the enterprise and reaps more environmental benefits. The numerical results verify the validity of the contract and the feasibility of the RPM.

Reward–Penalty Mechanism Based on Daily Energy Consumption for Net-Zero Energy Buildings

Net-zero energy buildings (ZEB/NZEB) have been greatly encouraged and are considered to be a promising approach for energy conservation as well as environmental protection. However, a lack of incentive mechanisms can hinder the fast development and application of ZEB. This study thus focuses on the design of a daily reward–penalty mechanism (RPM) by considering the performance of the building, aiming to enable a lower penalty cost for the building where there is a better match between energy consumption and energy generation. The impact of the degree of freedom of the building load (k) is investigated on building performance based on a single-family house located in Shanghai city, China. It is observed that a higher value of k is preferred since the building users can adjust its energy consumption profile to better match with its energy generation. A higher k value enables lower annual energy consumption, lower penalty cost, better stability, and an average daily zero energy level of around 1.0. In addition, four quadratic fit curves are derived to describe the relationship between building performance (i.e., annual energy consumption, the average daily zero energy level, stability, and annual penalty cost) and the degree of freedom. Meanwhile, the uncertainty of ZEB performance is quantified, which provides flexibility for building users in selecting the appropriate degree of freedom.

Comparative evaluation and policy analysis for recycling retired EV batteries with different collection modes

With the rapid adoption of Electric Vehicles (EVs), numerous lithium-ion batteries (LIBs) are reaching retirement age, leading to increasing concerns about the sustainable industrial development. To promote the recycling of LIBs, a reward-penalty mechanism is proposed in this work and analyzed by using the Stackelberg game theory. Six collection modes are considered and compared, including the collection activities undertaken by different stakeholders, i.e. the EV manufacturer, the EV retailer, the third-party enterprise, and the pairwise partners of them. The results show that: (i) a partnership between the manufacturer and retailer can achieve the highest actual collection rate and the total welfare; (ii) although increasing the reward-penalty intensity can contribute to higher collection rates, the total welfare can still fall, due to the negative influences of policy expenditure and implementation cost; (iii) the impact of raising the collection rate target is similar to the reward-penalty intensity, which can first promote and then restrain the growth of total welfare, and the drop is mainly due to the negative influences of corporate profits and implementation cost.

Impacts of altruistic preference and reward-penalty mechanism on decisions of E-commerce closed-loop supply chain

This study considers two incentive mechanisms, the reward-penalty mechanism (RPM) and the altruistic preference (AP). An E-commerce closed-loop supply chain consisting of a remanufacturer and a network recycling platform (the platform) is constructed to examine the recycling service and the quality improvement. Three models are developed to study how RPM and AP affect recycling decisions and models are also extended to multiple recycling periods to explore how profits and recycling quantity change with time under two incentive mechanisms. The findings show that the RPM and AP help to improve recycling service level, quality improvement level, and recycling quantity. However, under the influence of the platform's AP, the RPM is not always conducive to raising the recycling price. The influence of the platform's AP on recycling price is affected by the quality improvement cost parameter. Only when the RPM intensity is higher than the threshold, RPM can benefit remanufacturer. Consumer surplus is higher with AP and RPM both considered, so consumers can benefit from the two incentive mechanisms. The social welfare first increases and then decreases with the RPM intensity, but increases with the AP coefficient if the reference point of the remanufacturer's profit is small. Although the AP causes a decrease in the platform's profit in the early period of recycling, it can increase recycling quantity and the remanufacturer's profit over time. Moreover, both the remanufacturer and the platform can benefit from two incentive mechanisms and eventually obtain higher profits over time.

Comparison of two strategies of reward-penalty mechanism for promoting net zero energy buildings

Due to reduction of feed in tariff, the exploration of design solutions for smart cost-effective energy system is required to enable the consumers to select the optimum capacity of renewable energy system. This study investigates an innovative reward-penalty mechanism which is specifically designed to promote net zero energy buildings and net zero energy communities. Two different strategies (i.e. the building-level-based strategy and the community-level-based strategy) are developed regarding the application of the proposed reward-penalty mechanism. The electricity loads of family-houses in Ireland ranging from 1,286 kWh/y to 36,802 kWh/y are used to study the effect of the proposed strategies. The results show that both strategies lead to a similar cost versus zero-energy-building level relationship, i.e. a higher profit is related to a higher zero-energy-building level whilst a heavier financial penalty is related to a lower zero-energy-building level. The difference between the costs for the entire community (100 family-houses) predicted by the two different strategies is observed to be less than 10%. The building-level-based strategy is observed to produce the change rate of cost versus zero-energy-building level curve that often deviates significantly from the expected parabolic curve designed in this study, thus the community-level-based strategy provides an advantage over the building-level-based strategy. In addition, the Monte Carlo sampling method is used to quantify the uncertainty of the proposed reward-penalty function.

Research on Closed-Loop Supply Chain Decision-Making in Different Cooperation Modes with Government’s Reward-Penalty Mechanism

In closed-loop supply chains (CLSC), manufacturers, retailers, and recyclers perform their duties. Due to the asymmetry of information among enterprises, it is difficult for them to maximize efficiency and profits. To maximize the efficiency and profit of the CLSC, this study establishes five cooperation models of CLSC under the government‘s reward–penalty mechanism. We make decisions on wholesale prices, retail prices, transfer payment prices, and recovery rates relying on the Stackelberg game method and compare the optimal decisions. This paper analyzes the impact of the government reward-penalty mechanism on optimal decisions and how members in CLSC choose partners. We find that the government’s reward-penalty mechanism can effectively increase the recycling rate of used products and the total profit of the closed-loop supply chain. According to the calculation results of the models, under the government’s reward-penalty mechanism, the cooperation can improve the CLSC’s used products recycling capacity and profitability. In a supply chain, the more members participate in the cooperation, the higher profit the CLSC obtain. However, the cooperation mode of all members may lead to monopoly, which is not approved by government and customers.

Modeling and identification of suitable motivational mechanism in the collection system of municipal solid waste supply chain.

Many studies have identified that incentive, subsidy, and reward-penalty mechanisms improve the collection rate of recyclables and end of life products. But there is a lack of studies mathematical models and analysis of these mechanisms in the context of municipal solid waste supply chain. Therefore, in this study, models have been formulated for municipal solid waste supply chain (profit) considering government and collectors' profit under incentive, subsidy, and reward-penalty mechanisms. The study has analysed the models against the non-separation and separation scenario of waste. A numerical analysis is performed and observed that: (i) separation of waste at source along with incentive, subsidy, and reward-penalty mechanisms scenario improve the collection rate by 17%, 23%, 30%, and 45% compared to non-separated MSW. (ii) Incentive, subsidy, and reward-penalty mechanisms increases the total supply chain profit by around 9%, -36% and 18%. (iii) reward-penalty mechanism performs better than incentive and subsidy mechanism by providing the high supply chain profit (18% and 85%) and collection rate (22% and 15%) comparatively. Further, sensitivity analysis carried out to understand the behaviour of the models against the key parameters. The study also develops interesting propositions and proved for a better understanding of the models. From results, some key managerial insights have been drawn and a few future scopes of the study are presented.

Research on the influence of consumers’ environmental preference and government policy on EV battery recycling

According to Chinese government, the first peak period of EV battery recycling will come soon. Improper disposal of EV batteries will cause a serious damage on the environment. So, reverse supply chain of EV battery recycling should be established. As one of the main bodies of reverse supply chain, consumers play an important role in the recycling of EV batteries. Government policy can regulate EV battery recycling. These factors will have a direct impact on EV manufacturers recycling strategy. To discuss how these factors, influence the recycling strategy exactly, a mathematical is established. Results shows that compared with the government’s reward-penalty mechanism, consumers’ environmental preference is the fundamental driving force for EV manufacturers to recycle EV batteries.

Pricing and Collecting Decision of a Closed-Loop Supply Chain Under Market Segmentation With Reward-Penalty Mechanism

Pricing and Collecting Decision of a Closed-Loop Supply Chain Under Market Segmentation With Reward-Penalty Mechanism

Closed-loop Supply Chain with Advertising Investment and Reward-Penalty Mechanism Based on Different CSR Sharing Models

This paper considers a closed-loop supply chain (CLSC) consisting of a manufacturer and a retailer based on different corporate social responsibility (CSR) behaviors. In addition, the advertising investment and government’s reward-penalty mechanism (RPM) are also taken in account. To explore the impact of CSR behaviors on supply chain, we developed three models: (1) Only the manufacturer undertakes CSR (model M); (2) only the retailer undertakes CSR (model R); (3) the manufacturer and the retailer share CSR (model MR). By analyzing the results, we found that: (1) The advertising investment level and waste products return rate both increase with the increase of the extent to members undertake CSR when there is only retailer undertakes CSR and when the manufacturer and retailer share CSR, and remain unchanged when there is only the manufacturer undertakes CSR; (2) the overall social welfare increases with the increase of the extent to members undertake CSR no matter which member undertakes CSR, and reaches the highest when there is only the retailer undertakes CSR; (3) it is the most conductive for the performance of the closedloop supply chain when there is only the retailer undertakes CSR.

A Self-Adapting Task Scheduling Algorithm for Container Cloud Using Learning Automata

With the rapid development of cloud computing and container technology, more and more applications are deployed to the cloud, and the scale of cloud platform is expanding. Due to the large number of container instances running in the platform, complex dependency relationship, fast version iteration and other characteristics, the update of business can often cause the change of the whole cloud resource environment, which triggers the repetitive scheduling problem of related tasks and affects stability of the business. In this paper, we propose a self-adapting task scheduling algorithm (ADATSA) using learning automata to solve these problems. Firstly, we design a learning automata model and objective function for the system on task scheduling problem. Then, we realize an effective reward-penalty mechanism for scheduling actions in combination with the idle state of resources and the running state of tasks in the current environment. Meanwhile, the environment is modeled by cluster, node and task, and the probability of action selected is optimized by scheduling execution, thus enhancing the adaptability to the cloud environment of the scheduling and accelerating convergence. Finally, we construct a framework of task load monitoring with buffer queue to achieve dynamic scheduling based on priority. The experimental part verifies the effectiveness of proposed algorithm with different angles such as resource imbalance degree, resource residual degree and QoS. Compared with other learning automata scheduling models such as LAEAS, non-automata technology based algorithms such as PSOS and K8S scheduling engine, ADATSA shows the better performance of environment adaptability, resource optimization efficiency and QoS in dynamic scheduling. The theoretical analysis was consistent with the experimental results.

Research on an Enterprise Remanufacturing Strategy Based on Government Intervention

Due to rapid economic development and population growth, environmental pollution problems such as urban pollution and depletion of natural resources have become increasingly prominent. Municipal solid waste is part of these problems. However, waste is actually an improperly placed resource. As a part of green supply chain management, remanufacturing can turn waste products into remanufactured products for resale. Based on the development status of China’s remanufacturing industry, this paper establishes three Stackelberg game models, namely the free recycling model (model N), the government regulation model based on the reward–penalty mechanism (model G), and the government dual-intervention model (model GF). In this study, the standard solution method for the Stackelberg game method, namely the backward induction method, is applied to solve the dynamic game equilibrium. For comparison, a further numerical analysis is also carried. The research results show that: (1) in the closed-loop supply chain based on remanufacturing, the strengthening of cooperation between manufacturers and remanufacturers is beneficial in terms of maximizing supply chain profits; (2) in order to maximize social benefits, the government needs to intervene in green supply chain management; (3) government regulation is particularly important when the remanufacturing industry is in the initial stage of development; (4) government intervention needs to be based on the development level of the remanufacturing industry; (5) in order to maximize social benefits, it is recommended that the government consider the ratio between the green consumption subsidies and the taxes on new products.

Government Reward-Penalty Mechanism in Dual-Channel Closed-Loop Supply Chain

This study establishs a dual channel closed-loop supply chain (CLSC) model under a government–penalty mechanism (RPM) consisting a dual-channel manufacturer, a retailer, and the government. We consider a Stackelberg game between the manufacturer and the retailer, and the government rewards or punishes manufacturers on the basis of the collection rate of used products. This paper analyzes the influence of government RPM on the optimal decisions, the relationship between the two sales channels, and the total social welfare of the supply chain system. We find that the government RPM can improve the stability of the dual-channel supply chain and the collection rate of the used products. Moreover, we are the first to provide a method of deriving the optimal government RPM through a numerical example.

An Evolutionary Game Theory Study for Construction and Demolition Waste Recycling Considering Green Development Performance under the Chinese Government's Reward-Penalty Mechanism.

The low efficiency of the closed-loop supply chain in construction and demolition waste (CDW) recycling has restricted the green development of China’s construction industry. Additionally, the government’s reward–penalty mechanism has a huge influence on green development. This study aimed to investigate the effect of green development performance (GDP) and the government’s reward–penalty mechanism on the decision-making process of production and recycling units, as well as to reveal the optimal strategies under different conditions. Therefore, the strategies’ evolutionary paths of production and recycling units were investigated by using evolutionary game theory. Firstly, an evolutionary game model between production units and recycling units was proposed under the government’s reward–penalty mechanism. Then, the evolutionary stability strategies in different scenarios were discussed. Finally, the effects of the relevant parameters on the evolutionary paths of the game model were analyzed using numerical simulations. The main conclusions are as follows. (1) When the range of GDP changes, the evolutionary stable strategy changes accordingly. GDP plays a positive role in promoting the high-quality development of the CDW recycling supply chain, but an increase in GDP can easily lead to the simultaneous motivation of free-riding. (2) The government’s reward–penalty mechanism effectively regulates the decision-making process of production and recycling units. An increase in the subsidy rate and supervision probability helps to reduce free-riding behavior. Moreover, the incentive effect of the subsidy probability on recycling units is more obvious, while the effect of the supervision probability on improving the motivation of active participation for production units is more remarkable. This paper not only provides a decision-making basis to ensure production and recycling units to make optimal strategy choices under different conditions but also provides a reference for the government to formulate a reasonable reward–penalty mechanism that is conducive to a macro-control market.

Multi-product and multi-period closed loop supply chain network design under take-back legislation

This paper addresses the closed loop supply chain (CLSC) network design problem for durable products with consideration of take-back legislation. Each returned end-of-life product can be dismantled into different components. A recovery option is adopted for each component based on its quality level, its economic value, and its environmental impact. First, a multi-product multi-period mixed integer linear programming (MIP) model is proposed while assuming a 100% recovery target. The latter is then extended in order to incorporate other take-back legislations: namely, regulation based on other recovery targets and regulation based on reward-penalty (RP) mechanisms. Sensitivity analysis is then carried out in order to capture how some model parameters influence the take-back decision, the integration of the reverse supply chain, and its structure when there are no regulatory restrictions on the take-back of returns. In addition, the CLSC performance, in terms of environment and economic efficiency, is investigated under different take-back legislations. For the considered case study, a comparative study shows that a higher reverse service level and CLSC profit can be achieved when a regulation based on RP mechanism is implemented.

Can a Dynamic Reward–Penalty Mechanism Help the Implementation of Renewable Portfolio Standards under Information Asymmetry?

To further promote the low-carbon and sustainable development of China’s power industry, the Chinese government is vigorously introducing competition into power sales market. Simultaneously, On November 15, 2018, the National Development and Reform Commission issued the “Notice on Implementing the Renewable Portfolio Standards (Draft)” to propose the implementation of power sales side Renewable Portfolio Standards (RPS), which cannot be realized without an effective government regulation mechanism. However, information asymmetry and the limited rationality of the regulatory agencies and private power sales companies in the regulation process make the regulatory effect uncertain to the detriment of a sustainable regulation of the power industry. Thus, it is necessary to optimize the regulation mechanism of the RPS policy in China. We considered the competitive relationship between integrated power sales companies and independent power sales companies, and established an evolutionary game model based on a limited rationality. We also analyzed the implementation effects of the static reward penalty mechanism and dynamic reward penalty mechanism, respectively. The system dynamics (SD) simulation results showed that under the static reward penalty mechanism, there is no evolutionary stable equilibrium solution, and there will be volatility that exists in the evolution process. However, the dynamic reward penalty mechanism can effectively solve these problems. What is more, our results implied that governments should formulate appropriate RPS quotas, improve the green certificate trading mechanism, and take into account the market size of power sales while implementing RPS policy.

Research on Closed-Loop Supply Chain with Competing Retailers under Government Reward-Penalty Mechanism and Asymmetric Information

In manufacturer-led closed-loop supply chain (CLSC) with two competing retailers, the retailer-1 recycles WEEE whose fixed recycling cost is asymmetric information. Using dynamics game theory and principal-agent theory, three dynamic game models are built including (1) benchmark model without reward-penalty mechanism (RPM); (2) decentralized model with carbon emission RPM; (3) decentralized model with carbon emission RPM and recovery rate RPM. This paper discusses the influence of RPM and retailers competition on the CLSC and members benefits. The results show that (1) the carbon emission RPM increases retail price, but decreases the WEEE recycling motivation usually. On the contrary, the recovery rate RPM guides WEEE recycling and lowers the retail price effectively. (2) In any case, the retailer-1’s profit is higher than that of the retailer-2; apparently it suggests that the retailer recycling WEEE gains competitive advantages. Furthermore, both the recovery rate RPM and retailers competition are beneficial to improve the competitive advantage. The relationship between two retailers’ retail price is affected by many complicated factors. (3) The WEEE buyback price and WEEE recovery rate with high fixed recycling cost (H-type) are always higher than that of low fixed recycling cost (L-type), respectively, which means that the H-type fixed recycling cost has scale advantages; the greater the reward-penalty intensity and the fiercer the competition, the more obvious the scale advantages under certain condition. (4) The retailers’ competition can not only guide WEEE recycling but also improve retailers’ profits. Meanwhile, the impact of competition on the manufacturer is related to RPM, but the fierce competition decreases the manufacturer’s profit.

Economic Performance of Net-Zero Energy Community under Reward-Penalty Mechanism Considering PV System Reliability

Abstract Economic performance of net-zero energy building/community (ZEB/ZEC) is an important factor that affects potential investors’ decision on installing renewable energy systems (RES). A reward-penalty mechanism (RPM) is proposed for accelerating the development of zero energy communities, which is developed without considering the reliability effect from RES generation. However, an investigation is deserved for the reliability effect of RES on building economic performance. A case study is therefore conducted based on an assumed community consisting of 20 family houses, in which the electricity load was collected by the smart meter for more than one year. The results show that the proposed RPM works efficiently under an ideal condition, while the costs of the community and its buildings are greatly increased when the effect of PV system reliability is considered. Specifically, the total cost of the community under 1.0ZEC design is 5 005 USD/yr in the first year, which increases to 11 341 USD/yr in the 25th year. By contrast, the total cost of the community under 1.2ZEC design is 5 243 USD/yr in the first year and increases to 9 607 USD/yr in the 25th year. It is believed that the results of this study can provide a progressive perspective for scheme makers and building owners in terms of its economic benefit. Development of enhanced RPM by considering system reliability will be investigated in our future work.

Cost Allocation Model for Net-Zero Energy Buildings under Community-Based Reward Penalty Mechanism

Abstract The introduction of financial incentives for net-zero energy building/community (ZEB/ZEC) is a potential strategy that facilitates the development of sustainable buildings. In this study, a reward-penalty mechanism (RPM) is firstly proposed for a community that aims to achieve the target of annual zero energy balance. In order to investigate the cost allocated for each building in the community, a cost allocation model by considering the load of these buildings and the levels of zero energy building achieved is further proposed, based on which four typical types of the model is selected and investigated. The economic performance of a building under the four types of allocation model is then compared for a community that consists of 20 family houses in Ireland. By considering the possible ZEB level ranges in each building, two Cases are conducted (Case 1 – the range is between 0.0 and 1.0; Case 2 – the range is between 0.5 and 1.0). The results show that the 1st model is the simplest one that allocates cost evenly. By contrast, the cost of a building depends on its load in the 2nd model and depends on the ZEB level it achieved in the 3rd model, while it considers the two factors evenly in the 4th model. The proposed cost allocation model is expected to provide a basic guide for the designers of financial incentives as well as experts in the fields of net-zero energy buildings.