Incentive-based Programs Research Articles

Economic incentives and return migrant scholars: Evidence from a talent recruitment program in China

In global talent flows, developing countries tend to experience large brain drain to developed countries. To strengthen scientific capacity, many countries have initiated programs to attract overseas scientists to return in recent years. This study evaluates the effect of a large-scale talent recruitment program on return migration and scientific productivity in the home country. We focus on the Thousand Young Talents Program in China, the major source country of global talents in this century. We use unique data on institution-, publication-, and citation-based measures for faculty hired between 2000 and 2017 in the top mathematics departments in China. Regression results show that the recruitment program leads to significant increases in hires’ overseas educational background and scientific productivity. The effects of the program are concentrated in universities in the top tier and those located in the economically developed coastal regions. This implies that the recruitment initiative has widened the gaps in hire quality across universities. Somewhat surprisingly, scientific output of incumbents declined after being exposed to returnees, likely due to lack of collaboration. For policy implications, our study suggests that incentive-based talent programs can be an effective tool to turn brain drain into brain gain for developing countries. Furthermore, complementary policies to encourage faculty collaboration could magnify the benefits from return migration to knowledge production in the home country.

An integrated price- and incentive-based demand response program for smart residential buildings: A robust multi-objective model

Residential buildings consume a significant amount of energy, emphasizing the importance of optimizing energy usage. Demand-side management (DSM) helps consumers and producers manage energy consumption through incentives and pricing. This study develops a new mathematical model to manage DSM in smart residential buildings. Extant literature commonly considers only a single objective function, ignores uncertainties, and applies only one price- or incentive-based program to load management in smart residential buildings. This study develops a multi-objective mixed-integer linear programming (MILP) model that applies both price- and incentive-based programs and considers uncertainties. The objectives are cost reduction, peak load minimization, user comfort improvement, and load factor maximization. This model can manage optimal schedules for household appliances and power exchange within buildings. The study shows that participating in the incentive-based program in a four-household residential complex yielded a 2 % decrease in electricity costs and a 1 % reduction in peak load while upholding comfort and load factor levels compared to non-participation. When extended to an eight-household complex, potential benefits include an 8.3 % decrease in electricity cost and a 2.6 % reduction in peak load, highlighting the program’s effectiveness in residential energy management strategies.

Understanding factors influencing user engagement in incentive-based travel demand management program

Incentive-based travel demand management (IBTDM) has proven effective in mitigating traffic congestion. However, a comprehensive understanding of factors influencing user engagement in IBTDM is lacking due to limited empirical evidence from real-world applications. This study bridges this research gap by examining data from over 4,000 users in an actual IBTDM program, incenTrip, in the Washington, D.C.-Baltimore region. Employing Poisson-Tweedie generalized additive models to account for excess zeros and nonlinear relations, the study examines how home and work-related factors influence users’ enrollment and engagement, measured by the number of registrations, generated trips, and earned incentives at a census block group level. Results reveal that: 1) Urban areas with high population densities and low incomes attract more users and encourage more green travel. 2) Initial enrollment is higher among young, female, Asian, and highly-educated residents, although their subsequent engagement may not be sustained. 3) Workers in educational institutions and retail trades exhibit higher enrollment and maintain stronger engagement than other workers. 4) Well-developed transportation facilities and a higher density of points of interest near the users’ homes or workplaces substantially enhance program attractiveness. 5) Nonlinearities, particularly threshold effects, are observed across various relations analyzed. These findings have valuable policy implications for optimizing ongoing IBTDM programs and informing future initiatives. Policy recommendations include implementing targeted and progressive incentives, adopting combined TDM strategies, prioritizing user-friendly designs, fostering collaboration with employers, and employing nuanced policymaking.

Social welfare maximization with efficient energy management of community microgrid considering customer behavioral response using MDCLPIS

A novel residential demand response (DR) program is proposed to encourage microgrid customers to participate in demand reduction using a house energy consumption scheduler (HECS) to maximize social welfare by optimally reducing the cost of electricity and to receive maximum incentives. In this research, both Price-based and incentive-based DR programs are considered, and the demand-price elasticity approach is implemented to model them. To make the DR model more effective, customer behavioral response is taken into account, by leveling the customers according to their price elasticity. An optimized non-cooperative energy game theory framework and a Nash equilibrium strategy are employed to model customer response under a multi-time of use (TOU) pricing and incentive scheme, with the aim of achieving the best scheduling of home-controlled appliances while preserving customers’ privacy. This approach maximizes social welfare by minimizing the electricity consumption cost for customers while simultaneously saving on generation costs. The negative externality effect on price rate is internalized between the customers of different consumption levels using multi-dynamic consumption-level pricing and incentive scheme (MDCLPIS). The suggested model distinguishes between consumers’ behavior in response to changes in energy prices and their behavior in response to changes in incentives. Finally, numerical and simulation results demonstrate the suitability and effectiveness of the proposed model and its advantages in terms of customer saved cost by up to 21.26%, microgrid saved cost by up to 29.44%, and getting benefits by optimal scheduling of their load.

A multi-objective energy scheduling of the reconfigurable off-grid microgrid with electric vehicles using demand response program

Microgrids are essential in ensuring responsible energy consumption and production patterns to support and advance the United Nations’ sustainable development goals. However, due to the limited supply in the off-grid microgrids, an increase in the integration of electric vehicles (EVs) into electrical networks for their charging operation is expected to create a void between the energy supply and demand. Demand response (DR) programs provide a better solution to the energy management problem of microgrids. However, the difference in the objectives of the stakeholders of the off-grid microgrid can create an issue in providing optimal solutions to its energy scheduling problem. This paper focuses on the multi-objective energy scheduling strategy of the off-grid microgrid to manage its limited energy supply optimally using an incentive-based DR program. A fast heuristic approach is also presented in this work to reconfigure off-grid microgrid optimally during its hourly operations. Furthermore, Hong’s (2m+1) point estimation method is used in this work to consider the uncertainties in the EVs and other DR participants. The proposed work is analyzed on the 33-bus and 69-bus off-grid microgrid consisting of droop-controlled distributed generations, EV charging stations, and consumers with curtailable loads. For the day-ahead operation of the off-grid microgrids, the analysis shows that the proposed work reduces the operational cost of the two off-grid microgrids by 2.3% and 4.56%, respectively.

Intervene: An Incentive-based Program to Increase Nurse Manager Self-care and Resiliency Following the Covid-19 Pandemic

Intervene: An Incentive-based Program to Increase Nurse Manager Self-care and Resiliency Following the Covid-19 Pandemic

Environmental Regulation Design: Motivating Firms’ Clean Technology Investments With Penalties and Subsidies

The recently enacted Inflation Reduction Act (IRA) includes a number of incentive-based programs (e.g., tax credits) designed to motivate firms to develop new clean technologies for fighting climate change. However, the IRA also includes a fee firms incur for excessive methane emissions. This represents the first time the United States government has ever levied a fee on greenhouse gas emissions, and it raises an interesting research question—how should a budget-constrained regulator balance the use of both incentive and penalty-based levers for stimulating investment in clean technology development? In this paper, we examine a regulator’s optimal penalty and subsidy decisions for motivating firms to invest in clean technology development. We illustrate how the level of competitive intensity in the market can influence a budget-constrained regulator with multiple competing objectives—the environment, firm profits, and consumer welfare. We find that a subsidy is always beneficial, irrespective of the regulator’s objective. While imposing a firm penalty always benefits the environment, it always negatively impacts the sum of firm profits and consumer welfare. However, depending on the level of competition in the market, instances can occur where imposing a high penalty actually benefits total firm profits or consumer welfare (separately). Interestingly, a regulator that cares about all three dimensions of its objective equally, should always set the penalty to either its minimum or maximum value, depending on whether the environmental cost of the harmful product is high or low.

Perceived Life Satisfaction and Illegal Forest Use in the Virunga Landscape of Rwanda and Uganda

Understanding the direct and positive impact of conservation incentive programs in the tropics is essential. Typically, conservation incentive programs in the tropics aim to enhance residents’ access to material resources, with the ultimate goal of improving human well-being. These programs are also intended to reduce human-induced threats to wildlife. However, access to material livelihood resources as a means to improved life satisfaction is a human-centered goal that may or may not benefit wildlife conservation. Using structural equation modeling, this paper examines the potential of life satisfaction to significantly reduce illegal forest use behavior of residents neighboring the Virunga Landscape parks in Uganda and Rwanda. The findings indicate that improved life satisfaction significantly reduces livelihood-based illegal forest use. However, improved life satisfaction was not found to reduce illegal forest use driven by traditional values and practices. The findings also indicate that illegal forest use driven by traditional values and practices significantly increases illegal forest use for livelihoods. Ultimately, this study suggests that although incentive-based programs in the Virunga Landscape may be helping to reduce illegal forest use for livelihoods, they may not be addressing traditional forest use practices. This paper recommends supplementing conservation incentive programs with community education programs and law enforcement to effectively control the dimensions of illegal forest use and its negative impacts on wildlife.

The Social Dimensions of an Incentive-Based Urban Recycling Program: A Case-Study from Istanbul, Turkey

Incentive-based programs are increasingly becoming common in recycling promotion. These programs are usually designed on the premise that the incentives will nudge the participants’ behavior to be more pro-recycling so that they may continue to support recycling even if there is no reward presented anymore. The technical and economic aspects receive a great deal of attention, while the social elements do not usually receive the same level of attention as above. In this study, a survey was conducted to recognize the recycling consciousness of participants in an urban recycling promotional program. The urban recycling program analyzed in this study was administered by Zeytinburnu Municipality in Istanbul, Turkey. This program had about 10,000 active users at the time of this face-to-face survey conducted among 428 participants in 2021. Zeytinburnu is a densely populated municipality, with a greater majority residing in apartment buildings. The results revealed that about half of the survey population was spontaneous recyclers, and they would like to continue even if there was no reward, while the other half was in the program just for the reward. However, the findings also displayed the potential of the incentive mechanism to be used to enhance the pro-recycling behavior of its participants. This may be achieved through educational tools and providing solutions to ease the burden most apartment-dwelling participants carry with storage/transportation issues.

Barriers and facilitators to scaling up access to HIV pre-exposure prophylaxis among key populations: A qualitative study of the incentive-based PrEP Seguro program in Mexico

IntroductionDaily oral pre-exposure prophylaxis (PrEP) is highly safe and effective for HIV prevention, yet barriers to PrEP access and adherence persist among key populations. In Mexico, incentive-based pilot programs have been effective in improving PrEP adherence among male sex workers. Understanding the experiences of providers and program implementers is critical to integrating PrEP adherence programs as part of standard care in Mexico and similar settings. MethodsWe conducted 17 in-depth informational interviews with care providers and staff responsible for administering PrEP to key populations (men who have sex with men, male sex workers, transgender women) in Mexico City. Interviews explored successes and challenges surrounding current PrEP implementation, as well as adaptations that could facilitate national scale-up of PrEP programs in Mexico. Informant transcripts were analyzed using a hybrid inductive-deductive thematic analysis approach utilizing CFIR constructs for the initial codebook while allowing for inductive findings. ResultsThree key themes emerged from informant interviews as important for promoting PrEP programs in Mexico: 1) increasing individual PrEP access, 2) strengthening quality of care, and 3) improving organizational and structural support. ConclusionsPrEP in Mexico is currently only available in a few clinics with high patient populations, and siloed HIV services, stigma, and a lack of inter-organizational collaboration remain persistent barriers to PrEP uptake. Promoting government collaboration and increasing financial support for community-based organizations is needed to expand PrEP access. Tailored, destigmatizing information about PrEP services needs to be diffused among both staff and patients to strengthen care quality.

Testing an insurance-based monetary incentive program for exercise: RCT design and rationale

Participation in regular physical activity (PA) has numerous health benefits; however, as few as 10% of U.S. adults meet guidelines when device-based assessments of PA are used. The present paper presents the design and rationale for an RCT examining the efficacy of an exercise incentive program currently offered by at least three major US insurance companies, in which participants must attend a YMCA fitness facility at least 50 times within 6 months to receive an incentive. In a factorial design, incentive amount ($200, $100, $0) is crossed with a comparison of the standard gain-framed incentive program and a loss-framed incentive condition in which participants are told their membership fee is being held and will be returned or forfeited depending on their fitness facility attendance. Participants (N = 330) are randomized to gain-framed $100 incentive (n = 55), gain-framed $200 incentive (n = 55), loss-framed $100 incentive (n = 55), loss-framed $200 incentive (n = 55), or control (n = 110). Each participant is enrolled in the same condition for two consecutive 6-month periods for a total of 12 months per participant. The primary outcome is number of visits to the fitness facility over each 6-month period, verified by objective swipe-card data. Secondary outcomes include total moderate-to-vigorous PA (MVPA) over 7-day periods assessed at 3-month intervals through accelerometers (Actigraph wGT3x-BT) and self-report. Habit formation and anticipated regret are putative mediators and household income is a putative moderator of the incentive-based programs. A payer-perspective, within-trial cost-utility analysis will quantify the incremental costs per (a) quality-adjusted life year gained, (b) YMCA attendance, and (c) change in MVPA.

The impact of incentive-based programmes on job-shop scheduling with variable machine speeds

Given the high demand for energy in the manufacturing industry and the increasing use of renewable but volatile energy sources, it becomes increasingly important to coordinate production and energy availability. With the help of incentive-based programmes, grid operators can incentivise consumers to adjust power demand in critical situations such that grid stability is not threatened. On the consumer side, energy-efficient scheduling models can be used to make energy consumption more flexible. This paper proposes a bi-objective job-shop scheduling problem with variable machine speeds that aims on minimising the total energy consumption and total weighted tardiness simultaneously. We use a genetic algorithm to solve the model and derive Pareto frontiers to analyse the trade-off between both conflicting objectives. We gain insights into how incentive-based programmes can be integrated into machine scheduling models and analyse the potential interdependencies and benefits that result from this integration.

Enhancing Peak Shaving through Nonlinear Incentive-Based Demand Response: A Consumer-Centric Utility Optimization Approach

This paper presents a comparative study on the implementation of incentive-based demand response programs in power grid management, comparing the results obtained from linear and nonlinear models. The challenges faced by power grids in balancing supply and demand and managing price spikes during peak periods are addressed, and demand response programs are proposed as an effective strategy. The focus is on implementing incentive-based programs where load serving entities determine the optimal incentive price and demand reduction. A novel approach is presented for simulating consumer behavior based on the price elasticity of demand and consumers’ utility function, incorporating both linear and nonlinear economic models. The calculation of demand reduction aims to maximize consumers’ welfare, while the determination of the optimal incentive price maximizes the profit of the load serving entities. Real data are utilized, and the proposed models are implemented using the mixed-integer nonlinear programming (MINLP) method. The results demonstrate the effectiveness of providing incentives to consumers during peak hours compared to other approaches, with a comparison between the linear and nonlinear models.

Evolution of a Summer Peak Intelligent Controller (SPIC) for Residential Distribution Networks

Electricity demand has increased tremendously in recent years, due to the fact that all sectors require energy for their operation. Due to the increased amount of modern home appliances on the market, residential areas consume a significant amount of energy. This article focuses on the residential community to reduce peak load on residential distribution networks. Mostly, the residential consumer’s power demand increases more during the summer season due to many air conditioners (AC) operating in residential homes. This paper proposes a novel summer peak intelligent controller (SPIC) algorithm to reduce summer peak load in residential distribution transformers (RDT). This proposed SPIC algorithm is implemented in a multi-home energy management system (MHEMS) with a four-home hardware prototype and a real-time TNEB system. This hardware prototype is divided into two different cases, one with and one without taking user comfort into account. When considering consumer comfort, all residential homes reduce their peak load almost equally. The maximum and minimum contribution percentages in Case 2 are 29.82% and 19.30%, respectively. Additionally, the real-time TNEB system is addressed in two different cases: with and without incentive-based programs. In the real-time TNEB system during peak hours, the novel SPIC algorithm reduces peak demand in Case 1 by 113.70 kW, and Case 2 further reduces it to 118.80 kW. The peak load decrease in Case 2 during peak hours is 4.5% greater than in Case 1. In addition, we conducted a residential consumer opinion survey to validate the acceptance rate of the proposed design and algorithm.

Pricing strategies in a dual-channel green closed-loop supply chain considering incentivized recycling and circular economy

In recent years, significant attention has been dedicated to the design of closed-loop supply chains. However, opportunities exist to refine modeling approaches in alignment with circular economy principles. This refinement could foster the collaborative involvement of customers and retailers, resulting in a more sustainable operational framework. This study aims to address this gap, fostering waste reduction, resource conservation, and collaborative engagement in closed-loop supply chains. Thus, a multi-level, dual-channel green closed-loop supply chain that integrates circular economy principles is examined. We focus on implementing an incentive-based recycling program and examining various closed-loop supply chain settings: centralized, decentralized, and collaborative. We aim to determine optimal equilibrium prices, greening levels, and advertising strategies while considering the concurrent aspects of greening and incentivized recycling in today environmentally conscious era. By developing a demand function that integrates price elasticity, cross-price impacts, sustainability, and advertising, we evaluate performance through numerical examples and a case study in the clothing industry. The findings indicate that a collaborative approach surpasses a decentralized one regarding sustainability, guaranteeing profitability across the supply chain. The study also underscores the trade-offs between centralization and decentralization, with the former failing to ensure maximum individual profitability despite augmenting overall chain profits. Our findings can guide managers in reconciling profitability with sustainability when choosing supply chain structures, accentuating the value of collaborative strategies and green cost-sharing contracts. Furthermore, our results highlight that targeted advertising can stimulate demand for green products, and incentivizing circular economy practices through collaboration can yield positive environmental and economic outcomes.

Optimal stochastic scheduling in residential micro energy grids considering pumped-storage unit and demand response

Improving energy efficiency, production costs, greenhouse gas emissions, and reliability are some of the most important problems in the energy industry. Energy storage systems and demand-side management (DSM) programs are among the existing solutions for removing such problems. This study presents an improved micro energy grid (MEG) to reduce operating costs as well as greenhouse gas emissions using combined cooling, heating, and power (CCHP) systems, wind turbines (WTs), photovoltaic (PV) units, pumped-storage units, and heating and cooling storage units. In this system, pumped-storage units are employed to store energy. Price- and incentive-based programs are employed to obtain better performance on the demand side. Uncertainties of wind speed, solar power, electricity price, and electrical load are considered to achieve more accurate results. In this paper, the Latin hypercube sampling (LHS) method is applied to generate various scenarios. Then, K-means clustering approach is employed to reduce the number of scenarios. By producing and reducing the proposed scenario, the inherent uncertainty in renewables can be covered, which will result in a more efficient model. Meta-heuristic whale optimization algorithm (WOA) and Pareto model are used to solve single- and multi-objective functions and to optimally design the proposed structure considering contradictory functions, respectively. The features of the proposed developed algorithm are simplicity in execution and absence of derivative operator, increase of local and global search capability and consideration of objective functions with different nature. Moreover, fuzzy decision-making mechanism is proposed to select the optimal solution from the Pareto set of solutions. Finally, the proposed approach in different scenarios on the microgrid with various sources is discussed. The results indicate the proposed method properly obtains the optimal solution.

Optimal promotional mix and pricing when faced with uncertain product value

The rapid development of digital technology has facilitated firms to adopt a variety of innovative strategies for promoting their new products. However, as different promotion strategies have different implications on market reach, consumers’ information structure, and incurred cost, figuring out how to minimize wastage on marketing expenditure with a combination of multiple promotion strategies remains a challenging task. To provide insights into the efficient adoption of a promotional mix, we develop a stylized model that considers a firm selling an innovative product to a market wherein the product value is ex ante unknown to each consumer and the firm. The firm has two promotion strategies, hype advertising campaign (HAC) and referral reward program (RRP). Specifically, HAC refers to a basic publicity that aims only on raising product awareness through an extensive market reach. In contrast, in line with the pay-for-performance nature, RRP is an incentive-based program that offers rewards to the existing customers for bringing new buyers through consumer-to-consumer (C2C) referrals. Whereas both strategies allow previously ignorant consumers to learn of a product’s existence, due to the variation on the communication intensity, only those who become aware of the product through C2C referrals will be able to make an informed purchase decision. We find that both HAC and RRP should not always be adopted simultaneously since consumer referrals may intensify the wastage on HAC expenditure. The condition under which the promotional mix yields the optimal outcome and the circumstances when the firm should abandon either HAC or RRP are investigated.

Optimal scheduling of CCHP-based resilient energy distribution system considering active microgrids' multi-carrier energy transactions

This paper introduces a two-stage two-level optimization method for optimal day-ahead and real-time scheduling of multicarrier energy distribution systems and microgrids. The model considers the incentive-based and price-based demand response programs to encourage microgrids to transact electrical, heating, and cooling energy carriers with the energy distribution system, which is named hereafter as the energy system. Further, the model formulates the resilient operation of the energy system considering the energy transactions with the electrical, heating, and cooling markets. The main contribution of this paper is the integration of demand response procedures of microgrids in energy transactions with the energy system considering the switching of electrical switches and heating and cooling control valves. The optimization process is another contribution of this paper that is decomposed into two stages that consist of day-ahead and real-time horizons. The first stage is also decomposed into two levels that determine the optimal scheduling of the energy system and microgrids in day-ahead markets. The second stage is comprised of two levels that commit the energy system and microgrids resources. A resiliency index is proposed to assess the resiliency of the energy system in shock conditions. The proposed method was simulated for the 123-bus test system. Different types of microgrids, incentive-based and price-based demand response processes were considered. Simulation results confirmed that the proposed method can reduce the costs of residential, industrial, and commercial microgrids by about 4.47%, 3.88%, and 5.47% concerning only the real-time pricing process. Further, the model can increase the aggregated benefits of the energy system in the day-ahead and real-time markets by about 0.608 Million Monetary Units (MMUs) and 1.10 MMUs, respectively.

Closing the gender gap in academia? Evidence from an affirmative action program

This study investigates a unique incentive-based affirmative action program in Germany’s academic labor market. By analyzing a sample of business administration professors, we document that the probability that a newly tenured professor is female increases at universities that participate in this government program compared to universities that do not. By delving deeper into the mechanisms of the program, we show that program universities lowered the entry barrier for tenured professorships regarding publication records for new female professors. While favoring women, we show that the program had no harmful effects on male professors regarding the entry barrier to tenured professorships. Overall, we provide evidence of the effectiveness of financial incentives as a means of reducing female underrepresentation in academic labor markets.

Sustainable Production Scheduling with On-Site Intermittent Renewable Energy and Demand-Side Management: A Feed-Animal Case Study

By shifting towards renewable energy sources, manufacturing facilities can significantly reduce their carbon footprint. This environmental issue can be addressed by developing sustainable production through on-site renewable electricity generation and demand-side management policies. In this study, the energy required to power the manufacturing system is obtained from different energy sources: the conventional grid, on-site renewable energy, and an energy storage system. The main objective is to generate a production schedule for a flexible multi-process and multi-product manufacturing system that optimizes the utilization and procurement of electricity without affecting the final demand. A mathematical programming model is proposed to minimize both the total production costs and energy costs, considering a time-of-use pricing policy and an incentive-based program. The uncertainty in renewable energy generation, specifically under the worst-case scenario, is taken into account and the model is transformed into a robust two-stage optimization model. To solve this model, a decomposition approach based on a genetic algorithm is applied. The effectiveness of the proposed model and algorithm is tested on a real industry case involving feed-animal products. A sensitivity analysis is conducted by modifying problem parameters. Finally, a comparison with the nested Column and Constraint Generation algorithm is performed. The obtained results from these analyses validated the proposed model and algorithm.