Conventional Price Research Articles

Considering Differentiated Pricing Mechanisms for Multiple Power Levels

With the development of supercharging technology, charging stations will face supercharging piles and fast-charging piles coexisting for a long time. The traditional unified service fee pricing model will face the problem of slow-charging vehicles occupying supercharging piles, and the advantages of supercharging facilities cannot be fully utilized. Considering factors such as station revenue, station utilization rate, and user willingness, this paper constructs a differentiated pricing model for multi-power-level charging facilities based on capacity and electricity service fees. It reveals the impact of this pricing model on the economy and user satisfaction of charging stations. In addition, a multi-objective optimization model is constructed and solved by a multi-objective particle swarm method to determine the pricing strategy of different power levels that adapt to the actual situation. The results show that the differentiated pricing mechanism can increase the station revenue by about 10.74% and the station stack power utilization rate by about 7.14%, and user satisfaction is higher, which is better than the conventional pricing mechanism. Therefore, the proposed charging pricing strategy can provide a decision-making reference for charging station operation and planning.

Consumers´ willingness to pay for snacks enriched with insects: A trending and sustainable protein source

High-protein snacks appeal to consumers seeking to increase the protein intake in their diet. The high environmental impact of conventional protein sources is urging the need to explore more sustainable alternatives such as edible insects. In this study, the willingness to pay for yogurt enriched with insect protein was estimated. It also determined the effect of socio-demographics and extrinsic product attributes on the purchase decision and expenditure using the Heckman sample selection model. Data was collected through an online survey from 1,909 consumers in Catalonia, Spain using Qualtrics´ consumer panel. The results showed that 55.7% of the participants were willing to purchase the product and that the average premium price computed was 0.36€ or 24% extra compared to the conventional price of yogurt. Findings also revealed that the probability of purchasing yogurt enriched with insect protein increases with younger adults, male consumers, residing in rural areas, having a child member in the family unit, and an active employment situation. Moreover, expenditure increases when the importance of the local and ecological attributes of food is higher, while lower for the price attribute. The results of this study offer food producers valuable insights into consumers’ willingness to pay for novel protein snacks.

A joint solution framework for the optimal operation and evaluation of integrated energy systems based on exergy

Exergy analysis provides a unified scale for evaluating the quality and quantity of energy, and is suitable and essential for scientifically and rationally guiding the optimization and evaluation of integrated energy systems with intricate multi-component configurations. The present work proposes an exergy analysis model and joint solution framework for integrated energy systems based on exergy, and presents an efficient and accurate solution method. The exergy analysis model is analogous to the energy-based model and possesses a succinct form. This model is then applied to establish an input exergy model defining the exergy of inputs, a destroyed exergy model defining the exergy lost due to the conversions, transmissions, and storages, and a benefit exergy model defining the remaining useful exergy for the loads. The joint solution framework optimizes the system from the basis of exergy conservation, and evaluates the destroyed exergy of each link and the prices of the various exergy products from an economic perspective based on the established exergy analysis model. The complexity of the framework is then reduced by establishing a sequential optimization and evaluation model. The optimization model is solved accurately and efficiently using a stringent cutting plane method to address the problem of relaxation inaccuracy, and piecewise linearization to accommodate the non-convex constraints. Numerical computations on a 97-node system demonstrate the accuracy of the proposed models in exergy-based optimization, destruction, and price evaluation, preventing substantial deviations (e.g., a 23% discrepancy in total destroyed exergy). The adopted cost allocation method based on energy quality reflects fair pricing, rectifying conventional pricing disparities (e.g., a 47.56% underestimation of the electric load using energy-based pricing). Therefore, the accuracy and efficiency of the developed exergy analysis model, solution framework, and solution method are validated.

Coordinated pricing mechanism for parking clusters considering interval-guided uncertainty-aware strategies

With the increasing penetration of electric vehicles into the existing power networks, the development of proper energy management tools become essential to mitigate the damaging effects of emerging high-power charging modes. To facilitate the management of vehicles, they can be gathered into clusters in order to aggregate their charging profiles. This paradigm is called cluster of vehicles and can be forward developed by gathering a group of parking, thus forming a parking cluster where the characteristics of different parking lots are aggregated. In this paper, a coordinated charging price mechanism for clusters of parking lots is proposed. The new proposal is casted as a game-based framework which seeks for the equilibrium between the cluster coordinator and parking lots, instead of conventional tools where the pricing strategy is decided by a central entity disregarding the interests of users. The developed model is established as a bi-level optimization problem, which is further linearized to be tractable and solvable by off-the-shelf solvers. The implications of vehicle-to-grid are also studied by fully modelling bi-directional power flows in chargers. In addition, two uncertainty-aware strategies are proposed to cope with uncertain energy margins in parking lots. A case study is conducted and various results are analysed and discussed. It is shown that enabling vehicle-to-grid characteristics discloses significant economic benefits for users and the cluster coordinator. In addition, vehicle-to-grid impacts notably on the risk-averse character of the uncertainty-aware strategies proposed. The developed pricing mechanism is also compared with the conventional pricing policies based on key of repartitions, showing that the new proposal is able to reduce the cost for users, avoiding to directly translate the energy cost to charging points.

An optimization-based partial marginal pricing method to reduce excessive consumer payment in electricity markets

Among the pricing schemes, locational marginal pricing is the most widely adopted scheme in current electricity markets. Despite the nice properties, theoretical study and practical experience have shown that considering the strategic bidding behaviors of market participants, locational marginal pricing may result in excessive consumer payment and prejudices market efficiency. In this paper, we propose an optimization-based partial marginal pricing method to reduce consumer payment under strategic bidding. The advantages of the locational marginal pricing method are reserved to the best extent while the remaining problems are delicately handled. To achieve this, we first analyze the characteristics and problems of the locational marginal pricing scheme and present a partial marginal pricing structure. Second, we establish an optimization-based partial marginal pricing model, in which discriminatory price components are introduced in price formulation and the pricing properties including competitive equilibrium and revenue adequacy are formulated as constraints. Third, to verify the performance of the proposed pricing scheme considering the strategic bidding, a bi-level strategic bidding problem of the generator under the proposed pricing method is established and transformed into a mixed integer linear programming problem. The numerical results indicate that under the conventional pricing methods, the consumer payments under strategic bidding can be up to 16.67 times higher than those under truthful bidding, while the proposed method can effectively reduce excessive consumer payments by 92.1% while maintaining the desired pricing properties.

Inferring interactions in multispecies communities: The cryptocurrency market case.

We introduce a general framework for empirically detecting interactions in communities of entities characterized by different features. This approach is inspired by ideas and methods coming from ecology and finance and is applied to a large dataset extracted from the cryptocurrency market. The inter-species interaction network is constructed using a similarity measure based on the log-growth rate of the capitalizations of the cryptocurrency market. The detected relevant interactions are only of the cooperative type, and the network presents a well-defined clustered structure, with two practically disjointed communities. The first one is made up of highly capitalized cryptocurrencies that are tightly connected, and the second one is made up of small-cap cryptocurrencies that are loosely linked. This approach based on the log-growth rate, instead of the conventional price returns, seems to enhance the discriminative potential of the network representation, highlighting a modular structure with compact communities and a rich hierarchy that can be ascribed to different functional groups. In fact, inside the community of the more capitalized coins, we can distinguish between clusters composed of some of the more popular first-generation cryptocurrencies, and clusters made up of second-generation cryptocurrencies. Alternatively, we construct the network of directed interactions by using the partial correlations of the log-growth rate. This network displays the important centrality of Bitcoin, discloses a core cluster containing a branch with the most capitalized first-generation cryptocurrencies, and emphasizes interesting correspondences between the detected direct pair interactions and specific features of the related currencies. As risk strongly depends on the interaction structure of the cryptocurrency system, these results can be useful for assisting in hedging risks. The inferred network topology suggests fewer probable widespread contagions. Moreover, as the riskier coins do not strongly interact with the others, it is more difficult that they can drive the market to more fragile states.

Beyond fitness: The nature of selection acting through the constructive steps of lifecycles.

We address the problem of defining selection and extracting the adaptive part of evolutionary change, originally formalized by Fisher and Price. Conventionally selection and adaptation are defined through fitness attributed to genes or genotypes chosen as units of selection. The construction through fitness is known to suffer ambiguities and omissions as a theory of change due to selection. We construct an alternative framing in which units of selection and fitness are replaced as the main abstractions by formal lifecycle models and reproduction rates through genetically distinct lifecycle realizations. Graphical representations of lifecycles express relations among reproductive stages that cannot be assigned to any one unit of selection. The lifecycle partition refines the statistics of overall reproductive success and resolves modes of selection that fitness either excludes or distorts through additive projections. We derive the Price equation in the basis of lifecycle realizations and compare it to the conventional Price equation for additive fitness of organisms. We show how the lifecycle approach recovers fitnesses acting concurrently at multiple levels, or contrasts forms of competition within and between levels that are invisible to additive fitness. Defining selection through lifecycles recasts population genetics from an object-focused to a construction- and process-focused representation.

True cost accounting of organic and conventional food production

Agriculture is one of the world's biggest polluters. Consumers are misled towards demand of unsustainable and inadequately priced food products by an insufficient internalization of externalities. Shifting demand towards more sustainable dietary choices can lead to a sustainable transition of agri-food networks. In this study, we evaluate environmental damage economically: we combine environmental assessment of different food products with the internalization of their monetary impacts. Life Cycle Assessments are modeled for conventional and organic foods and different production scenarios. The quantified environmental impacts are combined with True Cost Accounting to adjust food prices according to their environmental impacts. Using this framework for 22 German agricultural products, we find that on average, crop production generates externalities of about €0.79 per kg for conventional and about €0.42 for organic products. Conventional milk and eggs cause additional costs of about €1.29 per kg on average in organic systems and about €1.10 in organic ones. Conventional and organic meat generate externalities of €4.42 and €4.22 per kg, respectively, with beef generating the highest costs of all. The environmental favorability of organic products is confirmed, but the resulting organic market prices after internalization still exceed conventional prices. Externalities represent a negative impact on societal welfare, which should be addressed by policies supporting transparent pricing approaches.

Generalizing the Stochastic Approach to Price Indexes

I show that conventional price index formulas and time‐dummy hedonic regression techniques all consistently estimate growth parameters in the same generalized model of product pricing. I then use that result to make two points. First, the “stochastic approach” is not a helpful tool for choosing price index formulas, because in its complete form it can justify any of them. Second, the literature uses flawed arguments for replacing time‐dummy hedonic regression with hedonic imputation. The time‐dummy method is an excellent, underrated option.

EVALUATING R&D PREMIUM IN THE INDIAN HEALTH AND PHARMACEUTICALS INDUSTRIES

The economic advantages of research and development (R&D) investment have shown conflicting results in empirical studies. This study aimed to examine a different approach, evaluating R&D premium and cross-sectional variability of equity returns, a particular case for the Indian healthcare industry. The primary motivation for this study arrived from the size of the healthcare industry, the world's third-largest and India's largest industry, and the investment made in R&D activities. Results demonstrated that India's annualized R&D premium was significantly greater than the current value, investment, profitability, and momentum premiums. It indicated that the new R&D risk factor in pricing models is a primary reference for Indian equity investors, particularly for companies with R&D spending. Results were robust in evaluating portfolio return using univariate and multivariate tests. Findings suggested that R&D augmented models outperformed conventional pricing models, denoting that the R&D factor undoubtedly revealed priced element and vital risk factor in designing pricing models for emerging countries like India. When adjusting to R&D investment and trading strategies, policymakers, and financial professionals should hereby evaluate their risk-return implications

Consumers' willingness to pay (WTP) for processed fluted pumpkin based on sensory attributes in North-Central, Nigeria

Although there are diverse kinds of leafy vegetables in Nigeria, fluted pumpkin is one of the most consumed. However, the high perishability of its leaves has been a source of concern, of which mild processing has been suggested in the literature as a way out. Several studies have been done on the production and profitability of fluted pumpkins. However, none exist on consumers' willingness to pay (WTP) for processed fluted pumpkins based on sensory/organoleptic attributes in Nigeria. This study, therefore, fills this gap by investigating consumers' perception of processed fluted pumpkin leaves, their WTP for processed fluted pumpkin leaves and the amount they are willing to pay, and the factors affecting their WTP. Data used in the study were obtained using a set of pretested structured questionnaires in an interview schedule, and it was administered to 120 consumers in Ilorin, North-central Nigeria. Descriptive statistics and logistic regression analysis were used to analyse the data. The logistics regression checked the factors affecting the WTP for the processed leaves. The study found evidence that consumers have a high perception of the sensory/organoleptic attributes – general acceptability, texture, colour, general appearance, and aroma - of the processed fluted pumpkin leaves; with 92.3% of them willing to pay for the processed fluted pumpkin leaves while 34.2% of the consumers were willing to pay a premium which was more than the conventional price of fresh fluted pumpkin leaves. The study also revealed that the perception of consumers about the sensory attributes of the processed pumpkins and their preferences for the processed leaves positively influence their willingness to pay. This shows that there could be a market for this product. Based on the study's findings, it is therefore recommended that further studies be carried out on processed fluted pumpkins to ascertain their suitability for scaling their production for the market. Key words: Willingness to pay, Perception, Sensory Attributes, Vegetables, Logistics Regression

Maximizing Global Cooling Potential in Carbon Dioxide Removal (CDR) Procurements: A Proposal for Tonne-Year Pricing

Recently, a consortium of companies including Stripe, Alphabet, Shopify, Meta, and McKinsey allocated US$925 million for advanced market commitments to kickstart the early-stage Carbon Dioxide Removal (CDR) market. We argue that it is now more important than ever to consider a Global Cooling Potential (GCP) perspective in corporate CDR procurements. Currently, CDR projects are evaluated and priced on a simple cost-per-tonne basis, which fails to monetize storage duration and can ultimately incentivize the large-scale procurement of short-duration CDR. However, the relative duration of carbon storage is a critical aspect of any CDR project given the implications for climate warming from growing atmospheric concentrations of carbon dioxide. In this perspective article, we apply tonne-year carbon pricing to Microsoft and Stripe's initial CDR procurements to demonstrate that a combination of tonne-year pricing and conventional pricing could produce a CDR portfolio that simultaneously prioritizes storage duration, volume, and temporal urgency, which are all important considerations for maximizing GCP.

Web scraping technique for producing Iranian consumer price index

Data pervasiveness was made possible by the advent of new technologies such as the Internet and the World Wide Web in every human and non-human activity. This created an exponential increase or data explosion in data generation, coined under the term Big data. Alternatively, Big Data sources can contribute to the reduction of the response burden or they can be used only to study some economic or social phenomena before designing a statistical survey which is inherently expensive to pilot. Also, incorporating Big Data sources into official statistics means maintaining a net competitive advantage and relevance of the official statistics products compared to those provided by a plethora of commercial players, with reference to large corporations that are active in the field of information technology. In this paper, the web scraping technique was used to extract the daily prices of the food and drinks products in order to replace them with conventional prices which had been used for price indices. Moreover, these sorts of new datasets enable us to calculate the indices in smaller time scales like weekly or daily basis in comparison to the conventional approach which is possible only on monthly basis. Although web scraping has its own problems, it is more economically friendly, accurate, and time-saving, especially in urban areas. Findings revealed that the web scraping technique can be applied as an effective alternative to conventional methods for CPI. Also, this technique can be used for other price statistics.

A Nonproportional Premium Rating Method for Construction Risks

Correct pricing of nonproportional (primary or excess of loss) insurance for construction risks must consider not only how the insured property values build up over time, but also how the probable maximum loss (PML) changes. Conventional pricing methods for static property risks cannot be employed for construction risks, since the latter are characterized by PML patterns that change over time, as well as evolving loss exposures and perils arising from the various phases of the construction project. A further complication arises when delay in startup (DSU) is covered, because a DSU loss is triggered by a property damage loss and both losses contribute jointly to the erosion of an excess layer. This article describes a pricing method with analysis of specific cases of interest, including guidelines for creating practical excess of loss rating models.

The Efficacy in the Treatment of Acute Closed Soft Tissue Injury by Using Acupuncture and Diclofenac in Football Players

This study was aim to investigate the clinical treatment of acupuncture combined with diclofenac in the treatment of acute soft tissue injury in football players. This paperdivided100 patients with acute closed soft tissue injury caused by football into control group and treatment group randomly, 50 cases in each group. The control group was usually treated with PRICE, and the treatment group use PRICE combined with acupuncture and diclofenac to cure. One course for two weeks, treatment requires two courses. Before and after the patient was included in the study, record the scores of dynamic pain intensity, static pain intensity, local pain intensity and local sensitivity, and local swelling assessment. Improve clinical outcomes and record the treatment results after 3 days, 8 days, and 15 days. The results showed that there was a significant difference in the degree of symptomatic relief between the control group and the treatment group on days 3, 8 and 15 after treatment (P < 0.05). The difference between the two groups has statistically significant. The good rate and effective indicators of the treatment group were 65.6% and 92.1%, which was better than the control group (P <0.05) significantly. The results showed that acupuncture combined with diclofenac treatment can improve the pain symptoms of patients with soft tissue injury significantly, and its clinical superiority and overall efficacy are better than conventional PRICE treatment.

Towards robust and speculation-reduction real estate pricing models based on a data-driven strategy

In many countries, real estate appraisal is based on conventional methods that rely on appraisers’ abilities to collect data, interpret it and model the price of a real estate property. With the increasing use of real estate online platforms and the large amount of information found therein, there exists the possibility of overcoming many drawbacks of conventional pricing models such as subjectivity, cost, unfairness, among others. In this paper we propose a data-driven real estate pricing model based on machine learning methods to estimate prices reducing human bias. We test the model with 178,865 flats listings from Bogotá, collected from 2016 to 2020. Results show that the proposed state-of-the-art model is robust and accurate in estimating real estate prices. This case study serves as an incentive for local governments from developing countries to discuss and build real estate pricing models based on large data sets that increases fairness for all the real estate market stakeholders and reduces price speculation.

Milk, Fertility and Udder Health Performance of Purebred Holstein and Three-Breed Rotational Crossbred Cows within French Farms: Insights on the Benefits of Functional Diversity.

Simple SummaryWhen implementing dairy crossbreeding in purebred Holstein (HO) herds, farmers expect to improve the overall herd performance. However, they lack knowledge about how to manage and benefit from the diversity of genetic classes generated by three-breed rotational crossbreeding, which firstly refers to the cohabitation of purebred HO and first- and second-generation crosses (F1 and G2, respectively) within the herd. This study aimed to compare milk production, reproduction and udder health performance of HO, F1 and G2 cows, and to estimate how their combination in different proportions in the herd affects its profitability. We found that HO, F1 and G2 had different and complementary performance profiles, with two main trends. First, HO had higher milk yield, while F1 and G2 crosses had better fertility performance. Second, F1 had win-win trade-offs between milk production, fertility and udder health compared to HO and G2. We showed that HO-F1 or HO-F1-G2 (below 30%) mixed herds could be more profitable than purebred HO or fully crossbred herds with a conventional milk price. These findings can be used for advising purebred HO farmers who wonder about the benefits and the ways of managing the diversity of animal entities generated by the use of dairy crossbreeding in their herds.Using three-breed rotational crossbreeding in a purebred Holstein (HO) herd raises two questions: Do the different genetic classes of cows generated by crossbreeding perform differently? Are there any economic benefits of combining them within a herd? This study aimed at comparing the performance between the different genetic classes resulting from the use of three-breed rotational crossbreeding, and simulating the effect of combining them on herd profitability. Based on a dataset of 14 French commercial dairy herds using three-bred rotational crossbreeding from a HO herd over a 10-year period, we defined three genetic classes according to the theoretical value of heterosis and the percentage of HO genes. We performed linear models and estimated least square means to compare HO cows and the first and second generation of crosses (F1 and G2, respectively) on eight performance characteristics related to milk yield and solids, udder health and fertility. We used these to simulate profitability of five herd compositions differing according to HO, F1 and G2 proportions. We showed that HO, F1 and G2 cows had different and complementary performance profiles. HO had a win-lost trade-off between milk yield and fertility, G2 had the opposite trade-off and F1 had a win-win trade-off. Differences regarding milk solids and udder health were less clear-cut. We highlighted that combining HO with F1 or with both F1 and G2 (below 30%) could be more profitable than using purebred HO or crossbred herds in a conventional milk price scenario. These findings provide evidence on the benefits of functional diversity generated from the use of dairy crossbreeding in dairy herds.

Development of a model to demonstrate the impact of National Institute of Health and Care Excellence cost-effectiveness assessment on health utility for targeted medicines.

Advances in medical technology have led to a better understanding of heterogeneity of diseases and patients, and to the development of targeted medicines. This development is beneficial to society but can come at an increased cost to pharmaceutical manufacturers due to the costs associated with developing and manufacturing a diagnostic test. For such medicines, the conventional pricing structure, where a therapy is approved if it is deemed cost-effective, may not appropriately incentivize targeted drug development. We model the decision-making processes for both the healthcare provider and the pharmaceutical manufacturer, capturing their main priorities, and populate it with information from a recent appraisal by the National Institute of Health and Care Excellence. Healthcare providers prefer a stratified drug to be developed for a subgroup of the population when the drug is on average effective in the subgroup but with a detrimental effect in the complement. Whilst pharmaceutical manufacturers' preferences are similar, regions of disagreement exist. We show how preferences can be aligned by either penalizing the development of a non-stratified drug or rewarding the development of a stratified drug. The cost and position of alignment depends on the true value of health to the healthcare provider, among other parameters.

Steering the aggregative behavior of noncooperative agents: A nudge framework

This paper considers the problem of steering the aggregative behavior of a population of noncooperative price-taking agents towards a desired behavior. Different from conventional pricing schemes where the price is fully available for design, we consider the scenario where a system regulator broadcasts a price prediction signal that can be different from the actual price incurred by the agents. The resulting reliability issues are taken into account by including trust dynamics in our model, implying that the agents will not blindly follow the signal sent by the regulator, but rather follow it based on the history of its accuracy, i.e, its deviation from the actual price. We present several nudge mechanisms to generate suitable price prediction signals that are able to steer the aggregative behavior of the agents to stationary as well as temporal desired aggregative behaviors. We provide analytical convergence guarantees for the resulting multi-components models. In particular, we prove that the proposed nudge mechanisms earn and maintain full trust of the agents, and the aggregative behavior converges to the desired one. The analytical results are complemented by a numerical case study of coordinated charging of plug-in electric vehicles.

The Correction of Multiscale Stochastic Volatility to American Put Option: An Asymptotic Approximation and Finite Difference Approach

It has been found that the surface of implied volatility has appeared in financial market embrace volatility “Smile” and volatility “Smirk” through the long-term observation. Compared to the conventional Black-Scholes option pricing models, it has been proved to provide more accurate results by stochastic volatility model in terms of the implied volatility, while the classic stochastic volatility model fails to capture the term structure phenomenon of volatility “Smirk.” More attempts have been made to correct for American put option price with incorporating a fast-scale stochastic volatility and a slow-scale stochastic volatility in this paper. Given that the combination in the process of multiscale volatility may lead to a high-dimensional differential equation, an asymptotic approximation method is employed to reduce the dimension in this paper. The numerical results of finite difference show that the multiscale volatility model can offer accurate explanations of the behavior of American put option price.