Price Schedule Research Articles

Securities Markets in Which Some Investors Receive Information About Cash Flow Betas

We analyze a single-factor setting in which there is private information regarding cash flows as well as their betas. Private information about betas, together with market makers’ risk aversion and mean betas’ nonnegativity, implies a nonlinear price schedule whose stochastic slope covaries positively with order flow when the expected factor payoff is positive and vice versa. We predict a negative relation between the covariance and expected returns and an attenuation of the beta anomaly in asset returns after accounting for this relation. Empirical tests confirm these predictions. This paper was accepted by Lukas Schmid, finance. Funding: L. Yang acknowledges the Social Sciences and Humanities Research Council of Canada [Grant 435-2021-0040] and Bank of Canada Fellowship for financial support. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.02061 .

Validation of a Model Predictive Control Strategy on a High Fidelity Building Emulator

In recent years, advanced controllers, including Model Predictive Control (MPC), have emerged as promising solutions to improve the efficiency of building energy systems. This paper explores the capabilities of MPC in handling multiple control objectives and constraints. A first MPC controller focuses on the task of ensuring thermal comfort in a residential house served by a heat pump while minimizing the operating costs when subject to different pricing schedules. A second MPC controller working on the same system tests the ability of MPC to deal with demand response events by enforcing a time-varying maximum power usage limitation signal from the electric grid. Furthermore, multiple combinations of the control parameters are tested in order to assess their influence on the controller performance. The controllers are tested on the BOPTEST framework, which offers standardized test cases in high-fidelity emulation models, and pre-defined baseline control strategies to allow fair comparisons also across different studies. Results show that MPC is able to handle multi-objective optimal control problems, reducing thermal comfort violations by between 66.9% and 82% and operational costs between 15.8% up to 20.1%, depending on the specific scenario analyzed. Moreover, MPC proves its capability to exploit the building thermal mass to shift heating power consumption, allowing the latter to adapt its time profile to time-varying constraints. The proposed methodology is based on technologically feasible steps that are intended to be easily transferred to large scale, in-field applications.

Energy subsidies: conceptual framework and measurement for distributional impacts in Argentina

Argentina has massively subsidized energy in recent decades, and the distributional impacts of those subsidies has been widely studied. However, regional disparities and public financing are two dimensions often omitted by previous research. We extend the analysis in these directions, focusing on the electricity sector. First, we develop a conceptual framework to formalize the deviation of prices from production costs. Second, by applying the “benefit incidence analysis” to household survey microdata, we measure subsidies at the household level and perform a distributional analysis. Our results indicate that regional disparities in electricity distribution costs and in the pricing schedules set by distribution companies are key drivers of the distributional impacts. Subsidies, as a percentage of the final price, vary between regions in the range of 50 to 80 percent. Additionally, omitting public financing leads to a bias in the perception of their redistributive effect: when subsidies are financed by a general consumption tax, the net benefits are less than half of the benefits from the subsidy alone. A series of globally relevant policy recommendations can be derived from the paper.

Project Acceleration Analysis Using the Crashing Method with The Alternative of Additional Labor and Additional Working Hours in Drainage Work

Project work delays often occur due to differences in site conditions, design changes, weather influences, and planning errors. The problem faced is the project duration, where if the project duration is accelerated or crashes, there will be several options to cut the project duration. This is the problem that becomes the topic of the thesis/journal, namely "Analysis of Project Acceleration with the Crashing Method with Alternatives for Adding Manpower and Additional Working Hours in Drainage Works". The author uses the primary method, with sources of RAB, Time Schedule, working drawings and analysis of city unit prices. As well as secondary research methods with sources, from relevant books, and project data such as price lists and wages. From the results of the study it can be concluded that the total wages of workers under normal conditions is IDR. 16,864,000.00 with the duration of project work for 30 working days. From the results of the analysis in this study, the total wages of workers with alternative labor additions of IDR. 19,672,000.00 with the duration of project construction work for 25 working days or 15.15% faster than the normal duration. Meanwhile, the total wage for workers with the alternative of additional working hours (overtime) is IDR. 25,052,300.00 with the duration of the project construction work for 25 working days or 15.15% faster than the normal duration. A more economical alternative to completing the project is the addition of manpower because it saves IDR. 2,228,000.00 from additional working hours (overtime), from normal wages.

The cost-efficiency carbon pricing puzzle

Any global temperature target must be translated into an intertemporal carbon budget and its associated cost-efficient carbon price schedule. Under the Hotelling’s rule without uncertainty, the growth rate of this price should be equal to the interest rate. It is therefore a puzzle that many cost-efficiency IAM models yield carbon prices that increase at an average real growth rate above 7% per year, a very large return for traders of carbon assets. I explore whether uncertainties surrounding the development of green technologies could solve this puzzle. I show that future marginal abatement costs and aggregate consumption are positively correlated. This justifies doing less for climate change than in the safe case, implying a smaller initial carbon price, and an expected growth rate of carbon price that is larger than the interest rate. In the benchmark calibration of my model, I obtain an equilibrium interest rate around 1% and an expected growth rate of carbon price around 3.5%, yielding an optimal carbon price above 200 USD/tCO2 within the next few years. I also show that the rigid carbon budget approach to cost-efficiency carbon pricing implies a large uncertainty surrounding the future carbon prices that support this constraint. I show that green investors should be compensated for this risk by a large risk premium embedded in the growth rate of expected carbon prices, rather than by a collar on carbon prices as often recommended.

Regional price differences of medical services: evidence from China

BackgroundPrice levels of medical services may vary across regions with different income levels, which would raise concerns about the equal access to medical services. This study aimed to estimate the spatial price index of medical services to measure price levels across 31 provincial regions in China.MethodsPrice data were collected from medical service price schedule in each region. Two methods based on the Purchasing Power Parities were used to estimate the spatial price index and measure price differences across regions. The two-way fixed effects models were used to examine the association between medical service price levels and income levels, and further investigate the impacts of price differences on utilization of medical services and medical expenditure.ResultsThe consistent estimation results were given by two methods. Medical service price level in the highest-price region was found to be 74% higher than the lowest. There was a significant negative correlation between price levels and income levels, as well as price levels and the utilization of outpatient services. Moreover, we also found a 1% increase in medical service price level was significantly associated with a 0.34% and 0.24% increase in the medical service expense per outpatient visit and per inpatient respectively.ConclusionsRegions in China had significant gaps in medical service price levels. Policymakers should pay more attention to regional price differences and take great measures such as enhancing financial protection to ensure the equal access to medical services and better achieve the universal health coverage.

Believe me when I say green! Heterogeneous expectations and climate policy uncertainty

We develop a dynamic model where heterogeneous firms take investment decisions depending on their beliefs on future carbon prices. A policy-maker announces a forward-looking carbon price schedule but can decide to default on its plans if perceived transition risks are high. We show that weak policy commitment, especially when combined with ambitious mitigation announcements, can trap the economy into a vicious circle of credibility loss, carbon-intensive investments and increasing risk perceptions, ultimately leading to a failure of the transition. The presence of behavioural frictions and heterogeneity - both in capital investment choices and in the assessment of the policy-maker's credibility - has strong non-linear effects on the transition dynamics and the emergence of ‘high-carbon traps’. We identify analytical conditions leading to a successful transition and provide a numerical application for the EU economy.

Reliability-driven time-of-use tariffs for efficient plug-in electric vehicle integration

Demand response (DR) plays a crucial role in balancing electricity supply and demand, but existing studies on price-based DR for plug-in electric vehicle (PEV) load management lack comprehensive considerations. Therefore, this study designs reliability-driven time-of-use (TOU) tariffs for the integration of PEVs, considering various factors such as random component failures, the stochastic characteristics of non-dispatchable components, and fluctuations in load patterns and uncertainties. The proposed framework employs advanced stochastic simulation models to assess generation facility reliability amidst extensive PEV deployment in three steps. First, individual components, including conventional generation capacity, renewable energy sources, and demand, as well as PEV load models, are developed. Then, a system reserve margin assessment is carried out via the generation of seasonal synthetic time series data. During this step, innovative techniques, including box-plot methods and expert rules, are used to simplify the representation of the system reserve margin at each hour. Finally, optimized TOU pricing schedules for PEVs are generated. These pricing hours are initially categorized as off-peak, mid-peak, or on-peak, streamlining scenario analysis and aligning PEV charging profiles with predefined pricing schedules for grid reliability evaluation. Case studies have shown that the proposed method can improve the reliability indices by effectively mitigating the impacts.

Combinable products, price discrimination, and collusion

We analyze the effect of different pricing schemes on the ability of horizontally differentiated firms to sustain collusion when customers are able to mix products to achieve a better match of their preferences. We compare the impacts on the likelihood of collusion and on consumer welfare from three pricing schemes: two-part tariffs, linear prices, and quantity-independent fixed fees. We find that a ban of either price component of the two-part tariff makes it more difficult to sustain collusion at profit-maximizing prices. We also find that whereas linear pricing is the most beneficial pricing schedule for customers in the absence of collusion, it is the most harmful pricing schedule for customers in the presence of collusion.

The impact of increase in block pricing on electricity demand responsiveness: Evidence from Ghana

The implementation of Increase in Block Pricing (IBP) policy is premised on the assumption that consumers adjust their behavior to save energy in response to electricity price increases. However, consumers often lack a comprehensive understanding of the pricing schedules, leading to inaccurate consumption predictions. This study utilizes monthly electricity billing data from the administrative records of urban residential consumers of the Electricity Company of Ghana Limited to estimate the causal effect of IBP policy schedules on electricity consumption. To achieve this, we employed the Regression Kink (RK) design identification strategy to explore the kink structure of the policy function. This paper finds an inelastic causal relationship between IBP policy and electricity consumption. The results show a treatment effect of 0.41 KWh with an associated p-value of 0.473 at a robust 95 % confidence interval. Thus, IBP policy has no causal effect on electricity consumption at the 50 KWh lower block threshold. Notably, electricity consumers do not respond significantly to price increments, rendering IBP policy ineffective in achieving energy saving at the lower block threshold. This research contributes to a deeper understanding of the effectiveness of IBP policy in promoting energy savings and informs researchers, policymakers, and stakeholders in the energy sector. Results can be used to develop evidence-based policies that contribute to energy conservation and climate change mitigation.

Nonlinear pricing in multidimensional context: An empirical analysis of energy consumption

Modern business practices frequently employ a blend of pricing strategies to segment markets effectively. As a result, consumers may encounter pricing schedules that are nonlinear and multidimensional. This paper presents a structural approach for estimating multidimensional nonlinear pricing models involving multiple decision variables in an energy market. Using a unique, rich panel dataset of Chinese household electricity consumption, we structurally estimate consumer preferences under the influence of an Increasing Block Price (IBP) and a Time-of-Use (ToU) system. Our structural approach allows us to distinguish and evaluate household-level price elasticities of demand, presenting a novel explanation for consumers' feedback on marginal price changes. Through model-based simulations, we demonstrate that a 1% increase in price corresponds to a 0.7% reduction in total electricity demand. However, our analysis indicates that practical opportunities for optimization within multi-dimensional pricing systems are limited. Our findings offer distinct insights into the complex interplay between intricate pricing structures and energy consumption behavior, thereby providing valuable guidance for policymakers and regulators.

Influence of the air-cooled supercritical carbon dioxide recompression cycle design-point on molten salt power tower techno-economic performance

This paper investigates the techno-economic performance of a molten salt power tower integrated with an air-cooled supercritical carbon dioxide recompression cycle. Prior research into the cycle design point showed trade-offs between efficiency, cost, and temperature difference across the heat input, resulting in trade-offs between the design-point generation of the molten salt power tower system and the cost of sensible-heat thermal energy storage. Prior research into cycle off-design showed that colder ambient temperature designs have higher efficiencies on cold days but lower efficiency and capacity degradation on hot days. We present a framework to evaluate how cycle design, cost, and corresponding off-design performance affect the techno-economic performance of the molten salt power tower system. We use a steady-state off-design cycle model assuming fixed shaft speeds, inventory control, and air-cooler fan power control and integrate the cycle model with the molten salt power tower model from the National Renewable Energy Laboratory’s System Advisor Model. First, we present a surrogate model that uses a heuristic approach to correct the interpolation when the cycle reaches capacity limitations at ambient temperatures hotter than design. Next, we evaluate how the recompression cycle design-point ambient temperature, air-cooler approach temperature, air-cooler fan power, and recuperator conductance influence cycle and system design metrics. Finally, we complete annual simulations using an hourly “duck curve” pricing schedule and present annual performance metrics to evaluate the cumulative effects of the cycle design parameters and corresponding off-design performance on molten salt power tower techno-economics.

Impact of building integrated photovoltaics on high rise office building in the Mediterranean

Building integrated photovoltaics (BIPV) presents a great opportunity for decreasing building energy demand and related CO2 emissions, specially in the refurbishment of old isolated high rise highly glazed office buildings. This article presents a simulation study of the impact of BIPV on a Spanish office building of the 60’s, aiming to serve as a reference for this type of buildings in the Mediterranean region. Upgrade of the envelope with BIPV is evaluated. The solutions for the glazing system include conventional solar control window and a transparent photovoltaic prototype window. On the opaque part, commercial BIPV is considered. A comprehensive model integrating the BIPV impact in walls in windows for the thermal, electrical and daylighting is presented. Dynamic simulations carried out with TRNSYS software allow to evaluate the impact on daylighting, energy demand and economics. The results show that transparent BIPV reduced the energy demand by 6.9% and the total energy balance by 21%. The opaque BIPV further improved these results achieving a 38.3% reduction in the energy balance. Moreover, transparent BIPV also reduces the hours with excessive daylighting, although at the cost of reduced daylighting autonomy. The economic analysis highlights the importance of electricity pricing schedules in the promotion of BIPV, comparing current tariff structure in Spain and the duck chart from California Independent Operator. Results show the capabilities of this technology and provides guidelines for investment cost and efficiency targets.

Advance Selling in the Wake of Entry*

This article provides a tractable model of inter‐temporal price‐discrimination by heterogeneous firms, imperative for our understanding of advance purchase markets in the wake of entry. The pricing schedule of an industry leader, whose product is more likely to match consumers' preferences, differs systematically from a newcomer's pricing. By diverting competition to a stage where consumers face uncertainty about their preferences, advance selling reduces prices while increasing the newcomer's market share and profitability relative to the industry leader. Policies curtailing firms' ability to sell in advance, although potentially beneficial for welfare, may consolidate an industry leader's position and reduce consumers' surplus.

Empirical analysis of network effects in nonlinear pricing data

Network effects, i.e., an agent's utility may depend on other agents' choices, appear in many contracting situations. Empirically assessing them faces two challenges: an endogeneity problem in contract choice and a reflection problem in network effects. This paper proposes a nonparametric approach to tackle both challenges by exploiting restriction conditions from both demand and supply sides. We illustrate our methodology in the yellow pages advertising industry. Using advertising purchases and nonlinear price schedules from seven directories in Toronto, we find positive network effects, which account for a substantial portion of the publisher's profit and businesses' surpluses. We finally conduct counterfactuals to assess the overall and distributional welfare effects of the nonlinear pricing scheme relative to an alternative linear pricing scheme with and without network effects.

Do retail businesses have efficient incentives to invest in public charging stations for electric vehicles?

Many public charging stations for electric vehicles in the United States are chargers installed at the premises of pre-existing businesses such as grocery stores or restaurants. This paper investigates the incentives of these retail businesses to install and operate charging stations. First, we note that observed pricing schedules for charging stations significantly depart from both first-best and monopoly pricing. We argue that a possible explanation is that many hosting facilities may install a charging station primarily as a strategy to attract more customers to their core business. Second, we use an imperfect competition model to study retail businesses’ incentives to invest in charging stations. We show that hosting facilities may be trapped in a prisoner’s dilemma where investing in a charging station decreases their profit in the long run. However, the equilibrium level of investments in public charging stations need not differ from the socially optimal outcome.

Tiered network health plans and changes in physician practice intensity.

To examine whether physicians in tiered physician networks where tier assignments are based on "intensity" of care, which is the quantity of resources used per-episode of care, change their intensity after learning detailed information about how their intensity compares to their peers. Administrative data on intensity and quality at the physician-episode level for all physicians included in a tiered physician network offered through the Massachusetts Group Insurance Commission (GIC) in 2010-2015. Data on physicians' share of revenue from GIC patients from the 2012 Massachusetts All-Payer Claims Database. For 21,086 physicians in seven specialties, we estimate the impact of the dissemination of detailed intensity performance information in 2014 on physician intensity per episode of care overall and decomposed into physician services, facility, and pharmaceutical subcomponents. Intensity outcomes were measured using a standardized price schedule. Using a difference-in-differences regression, we compared physicians with high exposure to the tiered network via a large share of their revenue coming from GIC patients ("GIC share") to physicians who were less exposed. Measures of intensity of care and GIC share were log-transformed, and models controlled for physician-episode type fixed effects. We linked GIC share to administrative data using National Provider Identifier. There were no statistically significant differences in total intensity of care with the informational intervention for physicians in procedure-based specialties (-0.12 elasticity of intensity per episode with respect to GIC patient share, 95% CI -0.30 to 0.06) or in relationship-based specialties (0.09, 95% CI -0.15 to 0.33). There were also no differences in intensity of subcomponents of care following the intervention. Tiered network incentives had no detectable impact on intensity of care that physicians provided to patients.

Nonlinear Pricing with Underutilization: A Theory of Multi-part Tariffs

We study the nonlinear pricing of goods whose usage generates revenue for the seller and of which buyers can freely dispose. The optimal price schedule is a multi-part tariff, featuring tiers within which buyers pay a marginal price of zero. We apply our model to digital goods, for which advertising, data generation, and network effects make usage valuable, but monitoring legitimate usage is infeasible. Our results rationalize common pricing schemes including free products, free trials, and unlimited subscriptions. The possibility of free disposal harms producer and consumer welfare and makes both less sensitive to changes in usage-based revenue and demand. (JEL D11, D21, D42, L86, M37)

Performance of prototype tandem UV filter and organic transparent photovoltaic windows

Building integrated photovoltaics (PV) are promising technologies to integrate renewable energy production and achieve positive energy buildings. Transparent photovoltaics increase the integration options, especially in windows and skylights. In this context, the Tech4win project has developed a prototype tandem UV filter and organic transparent photovoltaic. The current paper presents a techno-economic analysis of the performance of this prototype. The laboratory data is introduced in a TRNSYS18 simulation for evaluating the impact in the heating, cooling, lighting, and the PV electrical production. The simulation scenarios include office and residential buildings in five different climates. Moreover, the economic analysis consists of a sensitivity study on the PV window investment cost, the electricity price, and the feed-in tariff. The results show that the PV windows increase the heating and lighting demand in all cases, but may decrease the cooling demand compared to non-solar control windows. Consequently, in the heating dominated scenarios the PV windows increase the energy demand but, in cooling dominated cases, the demand only decreases if the PV window's solar heat gain coefficient (SHGC) improves that of conventional window. Nevertheless, in all studied scenarios the PV window improved the building energy balance. Finally, the electricity pricing schedules and the feed-in tariff are key into the economic feasibility.

Online transfer learning strategy for enhancing the scalability and deployment of deep reinforcement learning control in smart buildings

In recent years, advanced control strategies based on Deep Reinforcement Learning (DRL) proved to be effective in optimizing the management of integrated energy systems in buildings, reducing energy costs and improving indoor comfort conditions when compared to traditional reactive controllers. However, the scalability and implementation of DRL controllers are still limited since they require a considerable amount of time before converging to a near-optimal solution. This issue is currently addressed in literature through the offline pre-training of the DRL agent. However this solution results in two main critical issues: (1) the need to develop a building surrogate model to perform the training task, and (2) the need to perform a fine-tuning process over several training episodes to obtain a near-optimal control policy.In this context, this paper introduces an Online Transfer Learning (OTL) strategy that exploits two knowledge-sharing techniques, weight-initialization and imitation learning, to transfer a DRL control policy from a source office building to various target buildings in a simulation environment coupling EnergyPlus and Python.A DRL controller based on discrete Soft Actor–Critic (SAC) is trained on the source building to manage the operation of a cooling system consisting of a chiller and a thermal storage. Several target buildings are defined to benchmark the performance of the OTL strategy with that of a Rule-Based Controller (RBC) and two DRL-based control strategies, deployed in offline and online fashion. The strategy adopted for OTL emulates the real world implementation with a simulation process by implementing the transferred DRL agent for a single episode in the target buildings. Target buildings have the same geometrical features and are served by the same energy system as the source building, but differ in terms of weather conditions, electricity price schedules, occupancy patterns, and building envelope efficiency levels. The results show that the OTL strategy can reduce the cumulated sum of temperature violations on average by 50% and 80% respectively when compared to RBC and online DRL while enhancing the energy system operation with electricity cost savings ranging between 20% and 40%. The OTL agent performs slightly worse than the offline DRL controller but it does not require any modeling effort and can be implemented directly on target buildings emulating a real-world implementation.