Optimal Subsidy Research Articles

Dynamic policy in the presence of social norms

AbstractIndividual actions can depend on prevailing social norms. We investigate how optimal policy to promote pro‐social action should exploit the underlying social dynamics. We develop a dynamic model of prosocial action in which conformist consumers repeatedly choose whether to engage in some prosocial activity. Whereas individual behavior is not observed, the overall participation rate in the previous period is common knowledge. We demonstrate how conformity can lead to multiple steady states and how their selection depends on starting conditions and discount factors. We further show that the optimal subsidy path can be non‐monotonic and can decrease before reaching the steady state‐level. Our model thus provides a rationale for introductory subsidies to promote environmentally friendly behavior from a behavioral perspective.

Impacts of a sustainable aviation fuel mandate on airline competition — Full-service carrier vs. low-cost carrier

The aviation industry has proposed the collective goal of achieving net-zero carbon emissions by 2050. In addition to the existing market-based methods (MBMs), the use of renewable energies, in particular, sustainable aviation fuel (SAF), could be one of the most promising means for achieving this long-term target. This paper investigates how an SAF mandate from the government can lead to heterogeneous impacts on two types of airlines, namely, full-service carriers (FSCs) and low-cost carriers (LCCs), that compete in the same markets. A game-theoretic economic model is constructed that accounts for the different service quality levels and cost structures (i.e., fuel efficiency) between FSCs and LCCs. Our analytical results suggest that the SAF mandate favors LCCs over FSCs. The share of SAF blended into fuel raises the airfares of both types of airlines while reducing the traffic and profit of FSCs. The impacts on the LCC traffic and profit depend on its fuel efficiency relative to that of FSC. When LCCs are sufficiently fuel efficient, the SAF mandate can increase their traffic and profit. The government subsidy on airline's SAF use is also analyzed, deriving the optimal subsidy levels for FSC and LCC respectively to achieve pareto-improvement in social welfare and consumer surplus. We also examine how air passengers’ environmental awareness affects the outcomes of SAF mandate implementation. Our economic model also accounts for passengers’ heterogeneous preferences in terms of service quality and price sensitivity.

Vaccination subsidy allocation under budget constraints considering the human interpersonal contact pattern and vaccine protection effect in epidemics

Vaccination subsidy is one of the most important measures to increase the willingness of people to be vaccinated for epidemic prevention and control. Given the budget constraint of vaccination subsidy and the different roles of various age groups population in the epidemic transmission process, a novel approach is proposed to optimize the utilization of the limited vaccination subsidy budget. First, an age-structured SIR model that integrates the human interpersonal contact pattern and vaccine protection effect is developed to simulate the epidemic trend under a certain vaccination state. Then, a decision model is constructed to determine the optimal vaccination subsidy allocation scheme for various age groups based on the age-structured SIR model. A case study of Wuhan City is used to verify the decision approach, and a series of sensitivity analyses are conducted. The result shows that the peak time of an epidemic is positively related to the total treatment cost for all the infections, and more subsidies should be allocated to the populations with higher interpersonal contact frequencies for epidemic prevention and control.

A Stackelberg game framework for promoting battery-powered construction machinery through government subsidies

Diesel-powered construction machinery is a major source of harmful pollutants and carbon emissions. Technological advancements have made battery-powered construction machines (BPCMs) increasingly viable in operations. However, the high purchase prices of BPCMs represent a huge obstacle for contractors to embrace these low-emission alternatives. To facilitate the sustainable development of the construction industry, it is imperative for the government to implement effective subsidy allocation policies to promote the electrification of construction machinery. This paper proposes a Stackelberg game framework for optimal subsidy allocation. In the proposed framework, the government decides the subsidy amount offered to each type of BPCMs to minimize both pollutant emissions and carbon emissions. The contractors can then observe the government’s decision and make their optimal decisions regarding the purchase, operation, and replacement of construction machines accordingly to minimize their total costs. Contractors’ decisions in turn influence the government’s decision. Such an intricate framework has many appealing properties, which are analyzed in depth to provide useful managerial insights. Additionally, the design of effective subsidy policies by the government depends on a precise prediction of the contractors’ demand for construction machinery. To this end, a random forest machine learning model is developed. Real data were collected for model construction and testing. Statistical results and industrial comments show the high quality of our predictions.

Process Integration Technique for Targeting Carbon Credit Price Subsidy

Mitigating climate change requires a portfolio of strategies and the use of carbon dioxide removal techniques or negative emissions technologies (NETs) will be necessary to achieve to achieve this goal. However, the high implementation costs of advanced NETs lead to expensive carbon credits, hindering their broad acceptance and use. One potential solution involves governmental support through subsidies, aiming to boost the availability of NET-derived carbon credits. This research uses a graphical technique based on an extension of pinch analysis to identify the ideal subsidy level for carbon dioxide removal, taking into account factors such as carbon pricing, supply, and demand. The proposed approach modifies the limiting composite curve (LCC) methodology to accurately determine the optimal subsidy and establish the baseline amount of subsidized carbon dioxide removal needed. The approach enables the convenient and efficient construction of the LCC using a composite table algorithm. To illustrate the proposed methodology, two case studies composed of different NETs and demand sectors are investigated. The results show the most advantageous subsidy levels for these technologies, providing valuable insights to guide policymakers and investors in their decarbonization efforts. This research contributes to the development of effective governance and investment strategies by optimizing NET subsidy allocation. Such optimization is crucial for facilitating the widespread implementation of these technologies, which are in-line with the global efforts to mitigate climate change.

AI-based green technology implementation simulation for achieving carbon neutrality: exploring the role of subsidies and knowledge management.

This study investigates the role of green technology implementation (GTI) based on artificial intelligence (AI) at the household level to achieve carbon neutrality by addressing gaps in the existing research. The research focuses on understanding how education on green consumption preferences, green invention and emission impacts can optimally influence AI-based GTI decisions. Through behavioural analysis at the household level, this study quantifies the effects of education and preferences on emissions and proposes subsidies as accelerators for carbon-neutral transitions. Furthermore, the study employs regression analysis and simulation-based optimisation, which are then validated against prior methodologies, with a focus on Punjab, Pakistan. Utilising a simple random sampling technique, approximately 1000 households were surveyed to represent the province's diverse demographics comprehensively. Findings reveal that higher education levels correlate with less enthusiasm for AI-based GTI. Simulations measured optimal subsidy levels by striking a balance between encouraging green behaviour and technological adoption. By integrating diverse factors and AI-based GTI optimisation, this study defines important thresholds for education and subsidies, thus highlighting their pivotal role in advancing AI-based green technologies and sustainable household practices. This research significantly enhances the understanding of the complex relationship between AI-based GTI decisions and educational influences, thereby contributing to the advancement of environmental sustainability.

Decisions and coordination in low-carbon supply chains with a wholesale price constraint under government subsidies

Motivated by the challenge of meeting carbon-conscious consumers and carbon regulations, this paper expounded on the decisions and coordination in low-carbon supply chains with a wholesale price constraint (WPC). The WPC is the behavior of large retailers (e.g., Walmart, Amazon, and E.Leclerc) that pursue a profit margin that does not fall below a certain level, which can affect supply chain operations. First, we develop a competition model, revealing that the WPC can result in a loss of total supply chain profit and a shortage in carbon emission reduction and then pose systematic challenges. Following this, we design a Nash bargaining-based cooperation model, showing that it can coordinate the supply chain and achieve a continuous allocation of optimal profits according to the bargaining power of firms. Lastly, our extended analysis incorporates the external influence of government subsidies for low-carbon technologies and designs the optimal subsidy mechanism. We demonstrate that, while government subsidies can reduce cost pressures on the supplier and provide incentives to reduce carbon emission, they can also induce the retailer to set a higher WPC for greater profitability, so the performance loss remains prominent. In this circumstance, we construct a Nash bargaining-based cooperation model that can coordinate the supply chain. Overall, our insights can help firms achieve scientific carbon emission reduction and formulate a reasonable WPC, as well as provide a quantitative basis for government carbon regulations.

Optimal supply chain green innovation and subsidy strategy: producer subsidy versus consumer subsidy

PurposeConsidering two types of subsidies for producers (supplier and manufacturer) and one for consumers based on product greenness and sales quantity, this study aims to formulate optimal supply chain green innovation and subsidy strategies, and to achieve this goal with the support of information systems.Design/methodology/approachThis study introduces a composite green-product supply chain where suppliers focus on green innovation for component greenness and manufacturers focus on green innovation for manufacturing process greenness. Game theory modeling is applied to investigate the differences of product greenness, supply chain members’ profit and social welfare under different government subsidy strategies.FindingsIncreasing the unit greenness subsidy coefficient can boost product greenness and supply chain members’ profits, but does not always raise social welfare. When the government exclusively offers subsidies to producers, subsidies should be allocated to suppliers when there is a significant disparity in supply chain green innovation costs. Conversely, it is more beneficial to subsidize manufacturers. Consumer subsidies have the potential to enhance both environmental and economic performance in the supply chain compared with producer-exclusive subsidies, but may not always maximize social welfare when supply chain members have low unit costs associated with green innovation.Originality/valueThis study examines the optimal decisions for green supply chain innovation and government subsidy strategies. Supply chain members and the government can use the information system to collect and evaluate the cost of upstream and downstream green innovation, and then develop reasonable collaborative green innovation and subsidy strategies.

Technology innovation decision‐making and optimal government subsidy strategy with endogenous bargaining power in a vertical supply chain

AbstractThis study presents a vertical supply chain model that considers bargaining power to explore independent innovation and co‐innovation decisions and their impacts and analyzes the optimal subsidy strategy for co‐innovation. We find that collaboration promotes technology innovation. Increases in consumer innovation preferences and government subsidies are conducive to co‐innovation. Both independent and co‐innovation can promote Pareto improvements under limiting conditions. Government subsidies can improve co‐innovation performance, but the subsidy parameter needs to be regulated. The optimal subsidy strategy is influenced by the subsidy target and budget. Governments prefer an innovative degree subsidy or a production cost subsidy.

Fine-grained digital twin sharing framework for smart construction through an incentive mechanism

Prefabricated construction is increasingly applied in the construction industry. Recent academic and industrial efforts highlight that Digital Twin as a Service (DTaaS) is an innovative solution to strengthen the advantages of prefabricated construction by enabling continuous monitoring, precise control, and optimized maintenance of each unique module. However, the complexity of digital twins presents significant challenges for both platforms and subcontractors to apply and fully understand this technology. It is, therefore, crucial to develop a well-designed sharing environment where stakeholders volunteer to share their digital twin resources for synergistic benefits. This research proposes an innovative blockchain-based fine-grained digital twin sharing framework, with detailed architecture focused on two key stages of prefabricated construction: on-site construction and logistics involving warehousing and transport. An incentive mechanism is introduced to encourage all the stakeholders engaged in digital twin sharing framework to contribute and share more digital twin resources. Evolutionary and cooperative games explore the effective settings of the incentive policies, aiming to ensure the feasibility and sustainability of the proposed system by motivating users to share digital twins with fine granularity. Mathematical and numerical results demonstrate the optimal subsidy policies with/without budgetary constraints and further highlight the effectiveness of well-crafted incentives. Findings reveal that better performance in terms of profitability and digital twin sharing is achieved under the proposed incentives. Subsidies are primarily allocated to offset costs with a certain budget, whereas the emphasis shifts towards quality improvement under an ample budget. This study lays a solid foundation for digital twin reuse and development.

Optimal redistributive charity

AbstractThis paper analyzes optimal redistributive charitable giving in a Mirrleesian framework. The status motive behind charity, the stigma from receiving donations, and transaction costs all work to decrease the optimal marginal subsidies on redistributive charity. Because the existing empirical evidence of these effects (when available) is highly uncertain and context‐dependent, the range of possible outcomes is large, and even includes the possibility that charitable giving should be taxed rather than subsidized. In a natural benchmark case, the optimal tax treatment of charitable giving is consistent with a simple tax credit, whereas pure tax deductions are never part of the optimal policy.

Coordinative dispatching of shared and public transportation under passenger flow outburst

In the passenger flow outburst area (e.g., metro emergency, dispersal of sporting events, and concerts), passengers put forward large demand for ride-sourcing services. Insufficient ride-sourcing supply of the area induces increasing matching time, and the ride-sourcing influx from outside picking up passengers leads to heavier road traffic congestion in the flow outburst area. To solve this problem, we propose a novel coordinative dispatching method of ride-sourcing and multi-modal transit, in which subsidies and buses are provided for passengers in flow outburst areas to encourage taking other public transportation to low passenger flow areas first and then taking ride-sourcing to their destinations. In this paper, there are two options for passengers: (a) wait for ride-sourcing to pick them up; (b) take other public transportation first and then take ride-sourcing. To obtain the optimal dispatching and subsidy schemes, we develop a bi-level mixed integer programming model based on network flow theory and design the corresponding iterative algorithm to solve it. Considering the high uncertainty of ride-sourcing demand in the area, we further develop a robust optimization model to obtain more reliable schemes. Case studies based on a real-problem-scale dataset are conducted. The results demonstrate that our approach can be carried out in real time, and encouraging passengers to take multi-modal transit shows great potential in reducing affected users’ delays. The robust optimization model offers a more reliable and competitive solution when demand varies widely. Our method offers a win–win–win way for passengers, ride-sourcing service providers, and public transportation systems.

Optimal Vaccine Subsidies for Epidemic Diseases

Abstract We analyze optimal vaccine subsidies in a model integrating disease epidemiology into a market with rational economic agents. The focus is on an intensive vaccine campaign to quell an epidemic in the short run. Across a range of market structures, positive vaccine externalities and optimal subsidies peak for diseases that spread quickly, but not so quickly that everyone is driven to be vaccinated. We assess the practical relevance of this peak—as well as the existence of increasing social returns to vaccination and optimality of universal vaccination—in calibrations to the COVID-19 pandemic.

Can government subsidy promote the light-blue fishery upgrade to deep-blue fishery?

IntroductionThe expansion of fishery development from offshore to deep-sea areas has become essential for countries to address environmental constraints and ensure food security. This study aims to establish a cooperative development model for deep-blue fishery involving the government, fishery enterprises, and consumers.MethodsWe constructed an evolutionary game and simulation model to analyze the government's deep-blue fishery subsidy strategy. The model considers the interactions between the government, fishery enterprises, and consumers, evaluating how different subsidy strategies influence the development of deep-blue fishery.ResultsOur results demonstrate that government subsidies can stimulate the growth of deep-blue fishery from both the demand and supply sides. Specifically, the likelihood of government subsidies positively affects fishery enterprises' engagement in deep-blue fishery development and consumers' willingness to purchase deep-blue fishery products. The evolutionary game system reaches different steady states based on the cost-benefit changes for the government, fishery enterprises, and consumers, leading to various optimal subsidy strategies.DiscussionThe findings indicate that the amount of government subsidy should be optimized rather than maximized, as excessive subsidies may not yield proportionate benefits. Additionally, subsidizing fishery enterprises proves to be more effective in promoting the development of deep-blue fishery compared to subsidizing consumers. These insights can guide policymakers in designing efficient subsidy strategies to foster sustainable deep-sea fishery development.

Long‐term care and myopia: Optimal linear subsidies for private insurance

AbstractThis paper studies optimal linear policy directed at private long‐term care insurance in the context where individuals are myopic, that is, underestimate their dependency risk. Without government intervention, myopic individuals underinsure, while the first‐best optimum requires full insurance. To decentralize the first‐best, one needs personalized linear subsidies on private insurance premiums combined with personalized lump‐sum taxes to finance these subsidies and redistribute resources among individuals. In the second‐best setting where only uniform instruments can be used, the determination of the optimal insurance subsidy rate includes three main considerations: standard efficiency concern, correction for myopia, and redistributive concerns. While the correction for myopia pushes for a higher subsidy rate, the analysis of the redistributive concerns is far less straightforward. Overall, the redistributive concerns depend on three main factors: whether wealthier individuals are less myopic, how the probability of dependency varies with wealth and what is the type of absolute risk aversion exhibited by individual preferences.

Influence of subsidy policies against insurances on controlling the propagation of epidemic security risks in networks

Subsidies are used to further control the propagation of epidemic security risks as they offer users incentives to practice security investment behaviors. However, previously proposed subsidy policies have typically been studied without insurance, and it is still challenging to build effective subsidy strategies when users can transfer some of the infection loss to the insurer by purchasing insurance, because these external incentives may alter users' decisions to purchase insurance. To this end, we designed three subsidy policies in a scenario where purchasing insurance is one of the available strategies to users: under SPIns and SPInsp, the subsidies are used as the insurance funds and the risk precaution aspect of the insurance funds, respectively, and under SPInsd, a fraction of the subsidies are used to support some free-riding users with high node importance in their purchase of insurance, and the remaining subsidies are used as the precaution aspect of the insurance funds. The subsidy policies are studied with a formulated security investment game model on scale-free networks from the perspectives of the public and insurer. The results show that SPInsd can always work effectively and outperforms others in limiting the extent of an epidemic outbreak with similar or lower social costs, and without a reduction in profit for the insurer. More importantly, an interesting phenomenon emerges in the SPInsp scenario: under specific insurance parameters, the increase in subsidy funding has a negative impact on preventing the risk spread, leading to larger final epidemic sizes. In addition, the effectiveness of SPInsd on scale-free networks with larger average degree or network size and random networks are also studied. We anticipate this work can provide useful insights for policy makers with respect to design and implementation of optimal subsidy policies related to the control of epidemic security risks under an insurance scenario.

Non-additive network pricing with non-cooperative mobility service providers

This study addresses a mobility network pricing problem in a competitive environment. We consider a multimodal transportation network where the links are operated by multiple profit-maximizing, mobility service providers (MSPs). We take the perspective of a network regulator that aims to increase ridership in a target mobility network by providing non-additive, path-based subsidies to travelers. We model paths’ attractiveness using generalized cost functions that combine path travel time and path cost, and we use linear elastic travel demand functions to capture the proportion of demand served by a path. MSPs are non-cooperative and adjust link fares according to the subsidy policy implemented by the regulator. The goal of the network regulator is to solve a budget-constrained mobility network pricing problem under MSP competition. This game-theoretical framework is modeled as a single-leader multi-follower game (SLMFG) wherein the leader player represents the network regulator and multiple follower players represent the MSPs. We conduct a theoretical analysis of this SLMFG by identifying necessary and sufficient conditions for the existence of solutions to the parameterized generalized Nash equilibrium problem (GNEP) that is played amongst MSPs. We show that this GNEP is jointly convex and we use this property to develop an exact numerical approach to solve the SLMFG based on customized branch-and-bound algorithms. Numerical results reveal the impact of MSP competition in this mobility network pricing problem and shed novel insights into the design of optimal path-based subsidy policies.

Enhancing Transshipment Decision-making in Transportation Companies through Computational Intelligence: A Bayesian Game Model with Data-Driven Simulations at Guangzhou Port

The challenge of port congestion significantly impedes the efficiency of global trade flows and the economic vitality of hinterland regions. Addressing this issue, our study harnesses computational intelligence to construct a sophisticated Bayesian game model between the government and multiple transportation companies, where the transshipment costs, considered as private information to the companies, play a pivotal role. This research integrates data analytics and machine learning techniques to analyze the strategic decision-making processes of transportation companies, which decide on transshipment based on a critical cost threshold influenced by government subsidies and a probabilistic assessment of transshipment costs. Utilizing backward induction, the study outlines how the government can leverage computational models to devise an optimal subsidy strategy for transshipment, taking into account the anticipated responses of the transportation companies. The Bayesian Nash equilibrium identified through our model suggests that companies with costs below a predefined threshold are incentivized by government subsidies to opt for transshipment. This conclusion is further validated through evolutionary game theory analysis, enriched by data-driven simulations. Employing real-world data from Guangzhou Port, we conducted extensive computational simulations to quantify the impact of transshipment subsidies. The findings reveal a substantial alleviation in port congestion, with a 33.7% reduction in congestion levels and a 35% decrease in congestion-related costs, alongside a notable 1.9% increase in government revenue. These simulations, powered by advanced computational algorithms and data analytics, not only underscore the effectiveness of informed subsidy strategies in mitigating port congestion but also demonstrate the potential of integrating computational approaches in logistical and transportation decision-making. This study contributes a novel computational framework to the logistics and transportation literature, offering practical insights for policymakers to tackle the enduring problem of port congestion through data-driven strategies.

How to promote the sustainability of China's rural waste management system: Increase government subsidies or increase waste service management fees?

AbstractRural waste management is a highly complex issue requiring multi‐stakeholders' cooperation. The promotion of cooperative action through social mobilization is essential. Based on evolutionary game theory, this study constructs a dynamic model consisting of government, social funds, and rural residents to study the effectiveness of public–private partnership (PPP) projects in rural waste management in China. The results show that excessively high or low subsidies from the government to social funds are detrimental to the sustainability of rural waste management. The optimal subsidy rate ranges from 600,000 RMB to PPP 750,000 RMB. It may strike a balance between avoiding excessive financial burden on the government and encouraging a 5% increase in social funds' investment. Waste management fees are suitable for promoting the quality of rural waste management services, with the maximum ideal fee being 24 RMB. The simulation results also demonstrate that the policy combination of high incentives and high penalties and low supervision and high penalties is conducive to tripartite cooperation in PPP projects. In addition, the government should impose fines of more than 750,000 RMB on social funds to prevent potentially ineffective management services offered by the social funds.

Optimal government subsidy strategy for fresh agricultural products supply chain under adverse weather: a two-stage dynamic game model

ABSTRACT To investigate the effects of various government subsidies on the decision of agricultural products supply chain under adverse weather, this paper depicts the random profit function of adverse weather affecting the output of fresh agricultural products. Then, both the centralised and decentralised decision-making models are set with various government subsidies, i.e. no subsidy, output subsidy, procurement subsidy, one-time subsidy to company and one-time subsidy to farmer. Through comparative analysis and numerical analysis, we explore the effects of adverse weather and different government subsidies on the supply chain decisions including planting effort, procurement price and retail price.