Price Incentives Research Articles

The impact of implementing a consumption charge on carbon-intensive materials in Europe

ABSTRACT The production of basic materials accounts for around 25% of global greenhouse gas emissions. Existing measures to reduce emissions from industry are limited due to a combination of competitiveness concerns and a lack of technological options available to producers. In this paper, we assess the possibility of implementing a materials charge to reduce demand for basic industrial products and, hence, also reduce industrial emission levels. The modelling shows that a charge equivalent to around €80/tCO2 could reduce the EU’s total (energy plus process) CO2 emissions by up to 10% by 2050, depending on the substitution options available. The materials charge could lead to small GDP increases and a minor reduction in overall employment levels. Key policy insights Full carbon price pass through along the materials value chain creates incentives for resource efficiency and substitution in the value chain of material use. Most macroeconomic models ignore mitigation opportunities in the value chain, as do carbon pricing mechanisms for industrial emitters, which largely mute incentives for mitigation opportunities with free allowance allocation. Including consumption at a benchmark level in emission trading systems reinstates a full carbon price incentive for all mitigation opportunities while avoiding competitive distortions and carbon leakage risks. Macroeconomic modelling shows that this allows for an additional 10% emission reduction accompanied by a slight GDP increase and employment reduction. Long-term clarity on carbon leakage protection furthermore strengthens low-carbon investment frameworks.

Pricing policies in a dual-channel supply chain considering flexible return and energy-saving regulations

Due to the consistent growth of energy consumption and its impacts on production costs and the environment, governments have recently begun to devise and implement various energy-saving (ES) strategies. This paper studies the optimal pricing decisions in a dual-channel supply chain (SC) —consists of a manufacturer and a retailer— under different government intervention policies, namely ES, revenue seeking, social welfare, and sustainable development. We develop an extended dual-channel SC model under flexible return policies, including full-refund via the direct (i.e., manufacturer) channel, the indirect (i.e., retailer) channel, and both direct and indirect channels. A dynamic multi-level Stackelberg game approach is used to examine the SC interactions and characterize the return strategy, optimal pricing, and ES level decisions under different scenarios. The results show that employing full-refund via the indirect channel policy under price incentive schemes not only maximizes the profit of dual-channel SC but also satisfies the government’s objective functions. In contrast to other intervention policies, the revenue seeking policy does not necessarily improve the social welfare and energy saving level unless the government budget is increased.

Examining the Nutrient Profile and Price Incentive Structure of Combination Meals at Large U.S. Chain Restaurants (P04-159-19)

ObjectivesTo characterize the nutrient profile of combination meals and understand price incentives to upsize at large U.S. fast food and fast casual restaurants. MethodsCombination meals (n = 1479) from 34 U.S. fast food and fast casual restaurants were identified from online menus and corresponding nutrition information was obtained from a restaurant nutrition database (MenuStat). Three options for each meal were analyzed: 1) default (as advertised on menu), 2) minimum (low-calorie option), and 3) maximum (high-calorie option). The nutrient composition of meals was compared to the Healthier Restaurant Meal Guidelines, and linear models examined to what extent each meal component (entrée, side, beverage) drove differences in nutrients across meal options. Prices were obtained from restaurant websites for a subset of combination meals (n = 326) and linear models were used to examine the difference in total calories per dollar between default and maximum options. ResultsThere was substantial variation across combination meal options for calories (default: 1193 kcal; minimum: 767 kcal; maximum: 1685 kcal), saturated fat (14 g; 11 g; 19 g), sodium (2110 mg; 1783 mg; 2823 mg), and sugar (68 g; 10 g; 117 g). Most default meals exceeded the Healthier Restaurant Meal Guidelines for calories (97%) and sodium (99%); fewer exceeded the standards for saturated fat (50%) and total sugar (6%). Comparing maximum and default meals, beverages were the largest driver of differences in calories (178 kcal, 36% of difference) and sugar (46 g, 93%), and entrées were the largest driver of differences in saturated fat (3 g, 59%) and sodium (371 g, 52%). There were significantly more calories per dollar in maximum versus default meals (169 kcal/dollar vs. 138 kcal/dollar). ConclusionsDefault combination meals offered by large U.S. chain restaurants are high in calories, sodium, saturated fat, and sugar. Maximum meals offer significantly more calories per dollar compared to default meals, suggesting there is a strong financial incentive for consumers to “upsize” their orders. Industry and governmental policies that improve the nutritional profile of default meals and financially incentivize lower-calorie meals may be promising strategies for improving dietary behaviors in restaurants. Funding SourcesNone.

Do differences in reported expenditures between household scanner data and expenditure surveys matter in health policy research?

Household scanner data are increasingly used to inform health policy such as sugar-sweetened beverage taxes. This article examines whether differences in the level of reported expenditures between IRI Consumer Network scanner panel and the Consumer Expenditure Survey (CES) lead to important differences in demand elasticities and policy simulation outcomes. Using each dataset, we estimated a structural consumer demand system with seven food groups and a numéraire good. To compare the two datasets on a level playing field, we went to great lengths to ensure that the explanatory variables in the two demand models were comparably constructed. Results indicate that scanner data households are not consistently more price responsive than the general population and underreported Consumer Network expenditures do not seem to result in systematic differences in price elasticities. The income elasticities are uniformly lower in Consumer Network than in CES for higher income households because of the positive association between income and the degree of underreporting. This, however, has limited effects on uncompensated price elasticities and policy simulations because food budget shares are small for higher income households. Overall, these findings support continued use of household scanner data in health policy research related to effects of price (dis)incentives.

Breeding Biofortified Pearl Millet Varieties and Hybrids to Enhance Millet Markets for Human Nutrition

Pearl millet is an important food crop in the arid and semi-arid tropical regions of Africa and Asia. Iron and zinc deficiencies are widespread and serious public health problems worldwide, including in India and Africa. Biofortification is a cost-effective and sustainable agricultural strategy to address this problem. The aim of this review is to provide the current biofortification breeding status and future directions of the pearl millet for growing nutrition markets. Research on the pearl millet has shown that a large genetic variability (30–140 mg kg−1 Fe and 20–90 mg kg−1 Zn) available in this crop can be effectively utilized to develop high-yielding cultivars with high iron and zinc densities. Open-pollinated varieties (Dhanashakti) and hybrids (ICMH 1202, ICMH 1203 and ICMH 1301) of pearl millet with a high grain yield and high levels of iron (70–75 mg kg−1) and zinc (35–40 mg kg−1) densities have been developed and released first in India. Currently, India is growing > 70,000 ha of biofortified pearl millet, and furthermore more pipeline cultivars are under various stages of testing at the national (India) and international (west Africa) trials for a possible release. Until today, no special markets existed to promote biofortified varieties and hybrids as no incentive price to products existed to address food and nutritional insecurity simultaneously. The market demand is likely to increase only after an investment in crop breeding and the integration into the public distribution system, nutritional intervention schemes, private seed and food companies with strong mainstreaming nutritional policies. The following sections describe various aspects of breeding and market opportunity for addressing micronutrient malnutrition.

Consumer Inertia and Market Power

We study the pricing decision of firms in the presence of consumer inertia. Inertia, which can arise from habit formation, brand loyalty, and switching costs, generates dynamic pricing incentives. These incentives mediate the impact of competition on market power. For example, dynamic incentives can limit the equilibrium price effects of a horizontal merger. We develop an empirical model to estimate consumer inertia and dynamic pricing incentives using market-level data. We apply the model to a hypothetical merger of retail gasoline companies, and we find that a static model predicts greater price increases than those obtained while accounting for dynamics.

Incentive Mechanisms for Supplier Development in Mega Construction Projects

Supplier development (SD) has become an emerging supply chain management practice for firms focusing on their core strategic advantages by outsourcing a significant portion of production or construction activities. Various incentive mechanisms are available for suppliers to improve their capabilities to meet owners’ expectations. However, a comparative analysis is needed to understand how successful they are. This paper investigates the incentive mechanisms for SD in a construction supply chain that consists of one owner and one supplier. Considering the owner's dominant position in the negotiation of a supply contract, a principal–agent model with the objective of optimizing the owner's profit and a Stackelberg game model designed to optimize the profits of both the owner and supplier are proposed and analyzed. Two commonly used incentive methods, including purchase price incentive and cost sharing, are analyzed and compared. The analysis and comparison are illustrated by a numerical example. The primary contribution of this paper is the analysis of the impact of incentive methods on quality improvement for the supplier and a sensitivity analysis of the supplier's internal and external factors, such as purchase quantity, market demand, and manufacturing cost structures, which are useful managerial insights for SD practices in mega construction projects.

Documentation of the grain morphological variation within some traditional aromatic rice (Oryza sativa L.) varieties of West Bengal

With the introduction of high-yielding varieties, price incentives and research inclinations towards modern varieties of non-aromatic rice to achieve self reliance, cultivation of several indigenous scented rice varieties have been neglected. A large number of traditional scented rice has already been lost and many are on the verge of extinction. It is more so for the small and medium-grained non Basmati aromatic rice than the long-grained Basmati types which form the bulk of rice export. Even though the cultivation of traditional aromatic rice varieties in prime areas is fast declining, their aroma and grain quality is highly appreciated by local communities and they are used in several rituals and customs. Future progress in the improvement of scented rice largely depends on immediate documentation, evaluation, conservation of the extant scented rice genetic resources and their effective utilization. The present study is on the documentation and evaluation of some of the aromatic rice germplasm of West Bengal.

Ant Lion Optimizer for Optimum Economic Dispatch Considering Demand Response as a Visual Power Plant

Demand response is the reduction in power consumption of electric utility customers from their normal consumption to better match the demand for power with the supply. It comprises changes in consumption when the power system operators need it in order to balance the electrical grid. These changes are obtained by offering price incentives or any other way to encourage consumers to shift their demand peaks to higher energy production hours. The reduction in electric energy due to large consumers’ response can be considered as virtual power plants which can be added to the system capacity as a demand resource. In this paper, demand response is included in the optimal economic load dispatch problem as a demand resource. The demand response cost function is proposed in a similar way as the cost functions of conventional generation units. A new nature-inspired optimization algorithm called ant lion optimizer is used to calculate the optimum combination between both generations; conventional generators and demand response as a demand resource. The proposed approach takes into account two objectives; minimization of generation cost, and the emissions from the thermal power plants. The ant lion optimizer algorithm is coded and implemented in MATLAB environment. The proposed method is validated by applying it to two test systems; 6-bus system and IEEE-30 bus test system. The obtained results prove that the proposed method can significantly minimize both the operation and emissions costs of power systems while satisfying the specified constraints.

УДОСКОНАЛЕННЯ ТАРИФНОЇ ПОЛІТИКИ У СФЕРІ ЕЛЕКТРОЕНЕРГЕТИКИ ДЛЯ ДОСЯГНЕННЯ СЕКТОРАЛЬНОЇ ТА МАКРОЕКОНОМІЧНОЇ ЗБАЛАНСОВАНОСТІ

Розглянуто проблеми формування тарифної політики в умовах переходу на нову модель ринку електричної енергії в Україні та впровадження стимулюючого ціноутворення у сфері електроенергетики. Визначено основні вади наявної моделі ринку електроенергії; інституціональну структуру і функціональну взаємодію ринкових сегментів нової моделі ринку, заснованої на вільному ціноутворенні на електричну енергію як товар. Акцентовано увагу, що для досягнення секторальної та макроекономічної збалансованості необхідним є належне обгрунтування цін на послуги з розподілу електричної енергії в умовах переходу на нову модель ринку, оскільки тарифи на ці послуги і надалі регулюватимуться державою.Показано, що основними критеріями при формуванні адекватних тарифів в Україні мають бути: посильне фінансове навантаження на більшість учасників ринку електричної енергії з метою забезпечення його збалансованості; достатнє фінансове забезпечення для підтримання належного технічного стану енергетичної інфраструктури. Визначено головні недоліки наявної нормативної бази для впровадження стимулюючого ціноутворення на послуги з розподілу електричної енергії в Україні та причини, пов’язані з відстроченням застосування стимулюючого ціноутворення на дані послуги в Україні.Охарактеризовано основні складові елементи структури стимулюючого ціноутворення, які закладено в економіко-математичну модель з метою сценарного прогнозування фінансово-економічних показників сектору розподілу електричної енергії. Розкрито, що отримані за результатами моделювання прогнозні показники тарифів на розподіл електричної енергії для різних категорій споживачів, а також агреговані фінансово-економічні показники електророзподільних компаній, слід розглядати як галузеві орієнтири для прийняття рішень на рівні національного енергетичного регулятора.

Decentralized price incentive energy interaction management for interconnected microgrids

Interconnection of wide-area microgrids encounters a lot of challenges in energy management. Energy interaction among microgrids requires more extensibility and flexibility in communication and decision-making compared to energy management of a single microgrid. To address the problem, two distributed interaction algorithms for both islanded and grid-connected modes are proposed here, where price signal motivates aggregators to adjust power consumption and generation. Privacy-preserving gossip algorithm is designed to avoid disclosing local privacy in islanded interaction. Demand response (DR), as an important means of interaction, can optimize resource distribution and realize virtuous and stable market operation in grid-connected mode. Perturbation primal-dual sub-gradient method is therefore designed to optimize distributed demand response (DDR) considering global predetermined inequal constraints. Finally, taking an actual multiple-microgrid system as an example, simulation verifies the effectiveness and robustness under communication uncertainties.

Incentive-based demand response model for maximizing benefits of electricity retailers

The change of customer behaviors and the fluctuation of spot prices can affect the benefits of electricity retailers. To address this issue, an incentive-based demand response (DR) model involving the utility and elasticity of customers is proposed for maximizing the benefits of retailers. The benefits will increase by triggering an incentive price to influence customer behaviors to change their demand consumptions. The optimal reduction of customers is obtained by their own profit optimization model with a certain incentive price. Then, the sensitivity of incentive price on retailers’ benefits is analyzed and the optimal incentive price is obtained according to the DR model. The case study verifies the effectiveness of the proposed model.

Measuring Heterogeneous Price Effects for Home Acquisition Programs in At‐Risk Regions

Any entity offering flood insurance, whether it is private or government‐administered such as the National Flood Insurance Program (NFIP), faces the challenge of solvency. This is especially true for the NFIP, where homeowner affordability criteria limit the opportunity to charge fully risk‐based premiums. One solution is to remove the highest flood risk properties from the insurer's book of business. Acquisition (buyout) of flood‐prone structures is a potentially permanent solution that eliminates the highest risk properties while providing homeowners with financial assistance to relocate in a less risky location. To encourage participation, homeowners are offered a preflood fair market value of their damaged (or at risk of damage) structures. Although many factors have been shown to affect a homeowner's decision to accept an acquisition offer, very little research has been devoted to the influence of price or monetary incentive offered on homeowners' willingness to participate in acquisition programs. We estimate a pooled probit model and employ a bootstrap methodology to determine the effects of hypothetical home price offers on homeowners' acquisition decisions. We do so while controlling for environmental factors, property characteristics, and homeowner sociodemographic characteristics. Results show that price indeed has a positive effect on likelihood of accepting an acquisition contract. Furthermore, estimated homeowner supply curves differ significantly based on the damage status of the acquisition offer, as well as homeowner and property characteristics.

Corrigendum to “Shared Bicycle Scheduling Model Based on Price Incentive Mechanism”

Corrigendum to “Shared Bicycle Scheduling Model Based on Price Incentive Mechanism”

Effect of Food Expenditure on Farming Households’ Welfare in Osun State, Nigeria

Abstract Food and nutrition security remain Africa’s most fundamental challenge for human welfare and economic growth. In this study, recent survey data from Osun State, Nigeria, was used to examine the effect of food expenditure on farming households’ welfare in Nigeria. Logistic and OLS regression models were the analytical tools used. Food Insecurity Gap (FIG) and Squared Food Insecurity Gap (SFIG) were used to capture the severity of food insecurity among the households. The results showed that, all households sampled consume rice, beans, vegetable, fish and oil as basic food items, while only 32% of them consume potatoes. The regression results showed that the household size, per capita income, dependency ratio and age were the highly significant factors influencing food expenditure. However, the coping strategy that was mostly adopted by the farmers in the study area was to cut down on the numbers of food items consumed. Therefore, it was recommended that farm mechanization should be encouraged for optimal land use and productivity. In all, promoting agricultural policies with appropriate price incentives that focus on intensification, diversification and resource-stabilizing innovations will create more wealth for all categories of farming households and this in turn will ensure food security, especially in an era of economic deregulation.

Smart Home Cyberattack Detection Framework for Sponsor Incentive Attacks

Sponsor incentive pricing is an emerging pricing scheme in smart home energy systems. It allows a supplier of smart appliances to co-pay the electricity bills of customers due to using their products, which boosts the sales of smart appliances in the sponsor program. More importantly, it facilitates to shift energy usage of incentive customers from peak to non-peak hours by designing the time-varying rate, which is inversely proportional to the total demand. Despite its effectiveness in bill reduction and load balancing, the sponsor incentive pricing scheme is vulnerable to various forms of cyberattacks. In this paper, we develop the first detection technique for cyberattacks that exploits the sponsor incentive pricing scheme. Our short-term detection algorithm uses binary logistic regression for learning the energy usage patterns and identifying anomaly ones. Leveraging the partially observable Markov decision process framework, our long-term detection algorithm optimizes the utility’s decision of on-site inspections while minimizing the labor cost and the financial loss due to cyberattacks. The simulation results demonstrate that, for detection of rebate rate cyberattacks, the proposed algorithm reduces the peak-to-average ratio of the aggregate energy usage profile by 26.57% and 9.58%, compared with the no-detection scenario and a natural heuristic detection technique, respectively. For detection of sponsor incentive abuse, the maliciously elevated electricity bill is reduced by 46.03% and 15.30%, respectively.

China output up despite regulations

Our research examines the benefits and drawbacks for cooperatives who participate in voluntary coffee certifications. We interviewed administrators at twenty Costa Rican coffee cooperatives about management practices related to voluntary certification. Voluntary certifications are popular among coffee cooperatives. Access to certified markets is facilitated by state support of the cooperative sector, regulation of the coffee sector and well-enforced environmental and social laws. However, there are no significant or consistent financial incentives for farmers to pursue certification. Multiple certifications may lower auditing and implementation costs, but cooperatives rarely receive the full premium for multiply-certified coffee. Low market demand for certified coffee, weak price incentives and high auditing and management costs encourage cooperatives to certify only a portion of their members. This strategy rewards compliant farmers rather than inducing widespread change to farming practices among the entire membership. Though financial incentives are weak, certifications offer non-financial benefits to both farmers and cooperatives, including better management and more resilient cooperatives.

Hierarchical Optimization Method for Energy Scheduling of Multiple Microgrids

This paper proposes a hierarchical optimization method for the energy scheduling of multiple microgrids (MMGs) in the distribution network of power grids. An energy market operator (EMO) is constructed to regulate energy storage systems (ESSs) and load demands in MMGs. The optimization process is divided into two stages. In the first stage, each MG optimizes the scheduling of its own ESS within a rolling horizon control framework based on a long-term forecast of the local photovoltaic (PV) output, the local load demand and the price sent by the EMO. In the second stage, the EMO establishes an internal price incentive mechanism to maximize its own profits based on the load demand of each MG. The optimization problems in these two stages are solved using mixed integer programming (MIP) and Stackelberg game theory, respectively. Simulation results verified the effectiveness of the proposed method in terms of the promotion of energy trading and improvement of economic benefits of MMGs.

Your Uber Is Arriving: Managing On-Demand Workers Through Surge Pricing, Forecast Communication, and Worker Incentives

On-demand platforms (e.g., Uber, Lyft) often rely on independent workers, who are not directly under the platform’s control, to be available at the “right” time and locations to serve consumers at ...

Combining carbon pricing with LPG subsidy for promoting preservation and restoration of Uttarakhand forests

This paper explores the role of REDD+ programs in promoting community led preservation and restoration of forests in the Himalayan region of India. As there often exists a direct conflict between generating carbon credits through forest conservation and forestry-based livelihood sustenance activities, we consider the role of cooking gas subsidy in reducing communities' reliance on forests. Findings indicate that a price of 13 USD per ton of carbon dioxide equivalent can induce a switch away from fuelwood to lpg based cooking and promote carbon sequestration. However, for such an incentive to work, a community's combined earnings from non-timber forest products, farm income and livestock-based earnings also play an important role. Curtailing access to forests can reduce forestry-based incomes, which in turn, would require a higher carbon price incentive. If no subsidy is provided, carbon price would need to be increased to 27 USD per ton for a complete switch away from fuelwood consumption. Finally, afforestation in degraded lands can be successfully undertaken by the community when restoration costs are low at 2000 INR per ha. However, restoration costs exceeding 10,000 INR per ha or high forest fire risks are not conducive to community led restoration efforts even with the REDD+ incentives.