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To understand price incentives to upsize combination meals at fast-food restaurants by comparing the calories (i.e. kilocalories; 1 kcal = 4·184 kJ) per dollar of default combination meals (as advertised on the menu) with a higher-calorie version (created using realistic consumer additions and portion-size changes). Combination meals (lunch/dinner: n 258, breakfast: n 68, children's: n 34) and their prices were identified from online menus; corresponding nutrition information for each menu item was obtained from a restaurant nutrition database (MenuStat). Linear models were used to examine the difference in total calories per dollar between default and higher-calorie combination meals, overall and by restaurant. Ten large fast-food chain restaurants located in the fifteen most populous US cities in 2017-2018. None. There were significantly more calories per dollar in higher-calorie v. default combination meals for lunch/dinner (default: 577 kJ (138 kcal)/dollar, higher-calorie: 707 kJ (169 kcal)/dollar, difference: 130 kJ (31 kcal)/dollar, P < 0·001) and breakfast (default: 536 kJ (128 kcal)/dollar, higher-calorie: 607 kJ (145 kcal)/dollar, difference: 71 kJ (17 kcal)/dollar, P = 0·009). Results for children's meals were in the same direction but were not statistically significant (default: 536 kJ (128 kcal)/dollar, higher-calorie: 741 kJ (177 kcal)/dollar, difference: 205 kJ (49 kcal)/dollar, P = 0·053). Across restaurants, the percentage change in calories per dollar for higher-calorie v. default combination meals ranged from 0·1 % (Dunkin' Donuts) to 55·0 % (Subway). Higher-calorie combination meals in fast-food restaurants offer significantly more calories per dollar compared with default combination meals, suggesting there is a strong financial incentive for consumers to 'upsize' their orders. Future research should test price incentives for lower-calorie options to promote healthier restaurant choices.

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.

The growing penetration of electric vehicles (EVs) introduces both opportunities and challenges for power grid operators. Incentivization is considered a viable option to tempt EV owners to participate in supporting the grid during peak load intervals while receiving compensation for their services. Therefore, this study proposes a two-step incentive mechanism to reduce the peak load of the grid by enabling power trading among the microgrid, EVs and the utility grid. In the first step, an incentive price is determined for EVs considering the grid-loading conditions during different hours of the day. In the second step, a multi-objective optimization problem is formulated to optimize trading among different entities, such as EVs, the microgrid and the utility grid. The two objectives considered in this study are the operation cost of the microgrid and the revenue of EVs. Monte Carlo simulations are used to deal with uncertainties associated with EVs. Simulations are conducted to analyze the impact of different weight parameters on the energy-trading amount and operation cost of EVs and MG. In addition, a sensitivity analysis is conducted to analyze the impact of changes in the EV fleet size on the energy-trading amount and operation cost.

This study examines the effects of distortion of agricultural price incentives on agricultural value added in East Africa. The World Bank, IFPRI, FAO, and CSP are the sources of data. The dataset ranges from 1981 to 2018, and the error-corrected LSDV model is used to analyze the data. The results indicate that agricultural price incentives have positive and significant effects on agricultural value-added. Aggregate nominal assistance coefficient, exportable agricultural products nominal assistance coefficient, and nominal rate of protection have increased agricultural value-added significantly. Agricultural price incentives targeting different levels of value addition have larger effects than those targeting aggregate outputs. This implies that agricultural incentive policies and market conditions in support of local producers are vital to enhancing AVA in East Africa. Besides, larger areas of arable land, lower agricultural employment, a smaller population size, a larger GDP, less spending on education, and a better-performing polity contribute to a significant increase in the regional agricultural value added. The results generally imply that agricultural price incentives are vital to accelerating agricultural value addition in East Africa. Governments in this region should thus consider revising agricultural policies in a pro-agricultural way to further accelerate regional growth in agricultural value-added. Enhancing agricultural price support needs to be a crucial element of policy revisions in the region. In East Africa, agriculture is the main source of employment for a large section of the population. However, agricultural incentives have been reportedly distorted against agriculture, and sectoral income has been low. Consequently, farmers' income from agricultural value addition has been low. This study reports the effects of the distortion of agricultural incentives on agricultural value added in East Africa. The study shows that favorable agricultural incentives enhance agricultural value-added. The findings have strong implications for the region's smallholders, who are the subject of heavy taxation, either directly or indirectly. It will have far-reaching consequences for the poor, who rely on agriculture for a living. In particular, the findings influence regional anti-agricultural policy design, which is vital for the regional goal of achieving inclusive growth and structural transformation.

Considering the widening of the peak-valley difference in the power grid and the difficulty of the existing fixed time-of-use electricity price mechanism in meeting the energy demand of heterogeneous users at various moments or motivating users, the design of a reasonable dynamic pricing mechanism to actively engage users in demand response becomes imperative for power grid companies. For this purpose, a power grid-flexible load bilevel model is constructed based on dynamic pricing, where the leader is the dispatching center and the lower-level flexible load acts as the follower. Initially, an upper-level day-ahead dispatching model for the power grid is established, considering the lowest power grid dispatching cost as the objective function and incorporating the power grid-side constraints. Then, the lower level comprehensively considers the load characteristics of industrial load, energy storage, and data centers, and then establishes a lower-level flexible load operation model with the lowest user power-consuming cost as the objective function. Finally, the proposed method is validated using the IEEE-118 system, and the findings indicate that the dynamic pricing mechanism for peaking shaving and valley filling can effectively guide users to respond actively, thereby reducing the peak-valley difference and decreasing users’ purchasing costs.

We investigate the supply response for main staple food crops in Sub-Saharan Africa over the period 2005-2013 using an innovative dataset recently developed by FAO’s “Monitoring and Analysing Food and Agricultural Policies” (MAFAP) programme. Relying on dynamic panel techniques, we observe that acreage, production and yields respond to price signals, even if with a limited intensity. Moreover, we find that direct price incentives arising from border protection, government interventions in domestic markets, and price shocks at the border stimulate farmers’ supply. We also show that omitting transaction costs from the analysis leads to underestimation of the price elasticity of supply. Conversely, using wholesale instead of farm gate prices as proxy for producer prices leads to overestimation of this price elasticity.

Gradually replacing fossil-fueled vehicles in the transport sector with Electric Vehicles (EVs) may help ensure a sustainable future. With regard to the charging electric load of EVs, optimal scheduling of EV batteries, controlled by an aggregating agent, may provide flexibility and increase system efficiency. This work proposes a stochastic bilevel optimization problem based on the Stackelberg game to create price incentives that generate optimal trading plans for an EV aggregator in day-ahead, intra-day and real-time markets. The upper level represents the profit maximizer EV aggregator who participates in three sequential markets and is called a Stackelberg leader, while the second level represents the EV owner who aims at minimizing the EV charging cost, and who is called a Stackelberg follower. This formulation determines endogenously the profit-maximizing price levels constraint by cost-minimizing EV charging plans. To solve the proposed stochastic bilevel program, the second level is replaced by its optimality conditions. The strong duality theorem is deployed to substitute the complementary slackness condition. The final model is a stochastic convex problem which can be solved efficiently to determine the global optimality. Illustrative results are reported based on a small case with two vehicles. The numerical results rely on applying the proposed methodology to a large scale fleet of 100, 500, 1000 vehicles, which provides insights into the computational tractability of the current formulation.

PurposeThe purpose of this paper is to examine the factors influencing the choice and the amount of cocoa beans sold to public and private licensed buying companies in the Western North of Ghana.Design/methodology/approachThe study was conducted in the Western North of Ghana. Cragg’s Double Hurdle model was used to examine the factors influencing the choice of licensed buying company (LBC) whereas Kendall’s coefficient of concordance was employed in analysis of the marketing challenges.FindingsThe results showed that non-price incentives determine the choice and the amount cocoa beans sold to an LBC. Specifically, education, years of experience in cocoa farming and timely payment of sold cocoa beans positively influence the choice of public LBC. However, off farm job participation, provision of credit facilities and extension services affect the choice of private LBC as marketing outlet. Perceived low price of cocoa beans, inadequate credit support, and adjustment of scales used in weighing of cocoa beans were identified as the most important challenges confronting farmers.Research limitations/implicationsThe research provides important information on non-price incentives influencing cocoa marketing outlet decision as well as the marketing challenges faced by farmers which can contribute to improving internal marketing efficiency of the cocoa industry in Ghana. Besides, this study also extends the frontiers in terms of methodological approach by adopting Cragg’s Double Hurdle Model in addressing the research question.Originality/valueThe research provides important information on non-price incentives influencing cocoa marketing outlet decision as well as the marketing challenges faced by farmers which can contribute to improving internal marketing efficiency of the cocoa industry in Ghana. Besides, this study also extends the frontiers in terms of methodological approach by adopting Cragg’s Double Hurdle Model in addressing the research question.

Value chain–based approaches offer tremendous scope for market-based improvements in production, productivity, rural economy diversification, and household incomes, but are often covered by literature that is too conceptual or heavily focused on analysis. This has created a gap in the information available to planners, practitioners, and value chain participants. Furthermore, few references are available on how these approaches can be applied specifically to developing agriculture in Africa. 'Building Competitiveness in Africa's Agriculture: A Guide to Value Chain Concepts and Applications' describes practical implementation approaches and illustrates them with scores of real African agribusiness case studies. Using these examples, the 'Guide' presents a range of concepts, analytical tools, and methodologies centered on the value chain that can be used to design, implement, and evaluate agricultural and agribusiness development initiatives. It stresses principles of market focus, collaboration, information sharing, and innovation. The 'Guide' begins by examining core concepts and issues related to value chains. A brief literature review then focuses on five topics of particular relevance to African agricultural value chains. These topics address challenges faced by value chain participants and practitioners that resonate through the many cases described in the book. The core of the book presents methodological tools and approaches that blend important value chain concepts with the topics and with sound business principles. The tools and case studies have been selected for their usefulness in supporting market-driven, private-sector initiatives to improve value chains. The 'Guide' offers 13 implementation approaches, presented within the implementation cycle of a value chain program, followed by descriptions of actual cases. Roughly 60 percent of the examples are from Africa, while the rest come from Europe, Latin America, and Asia. The 'Guide' offers useful guidance to businesspeople, policy makers, representatives of farmer or trade organizations, and others who are engaged in agro-enterprise and agribusiness development. These readers will learn how to use value chain approaches in ways that can contribute to sound operational decisions, improved market linkage, and better results for enterprise and industry development.

Based on the data of 247 cities at the prefecture level in China from 2007 to 2019, this paper analyzes the impact of the carbon emissions trading CET pilot policy on carbon emission reduction from the perspective of the price mechanism and government constraints. The results show that the carbon emissions and carbon intensity in the pilot areas are significantly reduced by adjusting the industrial structure and promoting green technology innovation. In terms of regions, the emission reduction effect of the pilot policy in regions with a high proportion of industry is obviously weaker than that in other regions. The aim of the carbon emission trading policy in China that achieve carbon emission reduction is by coordinating the carbon emission trading price that fail to fulfill this aim independently and the degree of government punishment for enterprises.