Demand Curve Research Articles

The Impact of Aging on Housing Market: Evidence from China

In recent years, the Chinese government has gradually introduced the multi-child policy to control the aging phenomenon. The aging of the Chinese population has aroused international attention. How the aging of the Chinese population will affect housing prices makes this study focus on the question. This study fitted the housing demand curve using the CGSS database and found that the housing demand will continue to rise as residents enter middle age. After residents enter old age, housing demand shows a slow decline. Then, this study establishes the spatial Dubin model to analyze the influence mechanism of population aging on housing prices in China, and finds that aging has a positive impact on housing prices. However, with the intensification of the aging phenomenon in China, the benefits brought by the welfare housing distribution system to the elderly will be gradually consumed, and the insufficient supply of social labor force and the stagnation of total output, its positive impact on housing prices will gradually weaken or even become negative.

What are the pandemic's shocks on carbon emission trading? The different management applications.

Maintaining the stability of the carbon market is of great significance for China to meet its goal of "Double Carbon," but at the beginning of 2020 the COVID-19 pandemic emerged and the economy was greatly affected. A natural question is whether it impacted domestic carbon markets. This paper thus presents the event research method on eight carbon emission trading markets in China, because it can timely exhibit the benefits investors gained during the COVID-19 pandemic and also can overcome the difficulty of separating those benefits from the overall performance of the carbon market via high-frequency data. The results herein confirm that China's carbon market has reacted negatively to the COVID-19 pandemic, which mainly relates to the mandatory blockade and isolation policy adopted by the central government. The production and operation activities of enterprises decreased along with the demand for carbon quotas. Because of the panic, investors also had a negative attitude towards the carbon market, influencing the supply and demand curve of carbon quotas and causing a decline in carbon prices. Under the effectiveness of government epidemic prevention and control policies, we further find that the negative impact was gradually eliminated. Overall, our findings offer some important information for the decision-making of governments, carbon market investors, and policymakers.

Application of an Operating Reserve Demand Curve in ERCOT in the South Korean Electricity Market to Accommodate High Penetration Levels of Renewable Energy

Application of an Operating Reserve Demand Curve in ERCOT in the South Korean Electricity Market to Accommodate High Penetration Levels of Renewable Energy

Integrated optimization of multi‐vehicle‐type timetabling and scheduling to accommodate periodic passenger flow

AbstractA method was developed for integrating the process of multitype bus timetable generation and chains of trips formed by considering multiperiod passenger flow characteristics. First, the cumulative passenger demand curve was fitted and combined with the vehicle trajectory. Second, according to the results, the combined departure interval for multiple vehicle types was determined, and a one‐way possible timetable set was established. Third, considering the departure time window, the upward and downward possible timetable sets were connected. A bi‐objective mixed integer nonlinear programming model based on a spatiotemporal network was developed through two‐way matching. The objective of the model was to minimize the generalized fleet cost (a problem solved by the continuous deficit function of multiple vehicle types) and passenger waiting time. For two‐way matching, the computational complexity was reduced according to the postroot jump traversal rule, and the 𝑘‐shortest path algorithm was used to solve the bi‐objective Pareto‐optimal solution set. Finally, the Harbin No. 96 bus line was used as an example to validate the proposed model and algorithm. The optimization reduced the bus purchase cost by 51.28%, energy loss cost by 31.78%, and passenger waiting time by 37.07%, indicating that the proposed model can significantly reduce the costs for bus companies and passengers.

Left-Digit Bias at Lyft

Abstract Left-digit bias (or 99-cent pricing) has been discussed extensively in economics, psychology, and marketing. Despite this, we show that the rideshare company, Lyft, was not using a 99-cent pricing strategy prior to our study. Based on observational data from over 600 million Lyft sessions followed by a field experiment conducted with 21 million Lyft passengers, we provide evidence of large discontinuities in demand at dollar values. Approximately half of the downward slope of the demand curve occurs discontinuously as the price of a ride drops below a dollar value (e.g. $14.00 to $13.99). If our short-run estimates persist in the longer run, we calculate that Lyft could increase its profits by roughly $160M per year by employing a left-digit bias pricing strategy. Our results showcase the robustness of an important behavioral bias for a large, modern company and its persistence in a highly competitive market.

Revenue Analytics: The Problem With Fixed-Tier Pricing

With the widely used fixed-tier computerized pricing system (e.g., based on the best available rate or BAR), fenced discount rates are set and updated as a fixed percentage of the base rate such as the BAR. This intuitive computer-automated solution to a complex pricing issue is, however, theoretically suboptimal. The study demonstrates why the practice of using fixed-tier pricing is suboptimal, showing that this fixed-tier approach is inferior even when the initial set of fenced rates is optimal and even in the unlikely scenario of the various market segments’ demand curves shifting proportionally. As such, practitioners should avoid using a convenient fixed-tier pricing model (BAR-based or not) where only one pricing optimization is run and the rest of the fenced prices are calculated based on this optimized price using fixed percentages. Instead, a fenced-rate pricing system where individual segments are treated independently, and optimizations are run for each segment should be adopted.

Food Donations, Retail Operations, and Retail Pricing

Problem definition: For grocery retailers, managing perishable food that is nearing expiry is a major challenge. Donating food to food banks is socially responsible, as it improves local communities and reduces waste generation. It also diverts food to a secondary, quality-differentiated market. Academic/practical relevance: In this paper, we quantify the economic impacts of this secondary market for food by examining donation and pricing behaviors for competing retailers. Methodology: We use a structural model of price-discriminating oligopoly retailers to study the effect of food donations on store and category-level demand and equilibrium prices. Empirically, we estimate the food donation effect using a unique data set of food donations and sales for several categories of perishable foods across two major retail chains that compete in the same market. Results: The competitive effects of food donations follow from the price-discrimination logic. First, food donations create a direct demand effect. Donations raise the average quality of products on display, shifting demand curves inward and rotating them clockwise (e.g., more inelastic). Second, food donations create a market enhancement effect, softening price competition and raising equilibrium prices among competing retailers. Managerial implications: Food donations increase food prices and store profits, tying the socially responsible option to an economic benefit. This study contributes a new type of reuse operation to the literature on closed-loop supply chains.Funding: Funding from the Agriculture and Food Research Initiative (National Institute for Food and Agriculture, U.S. Department of Agriculture) [Grant 2020-67021-31377] is acknowledged.Supplemental Material: The online supplement is available at https://doi.org/10.1287/msom.2022.1185 .

Interest in and perceived effectiveness of contingency management among alcohol drinkers using behavioral economic purchase tasks.

Contingency management (CM), in which financial incentives are provided upon verification of abstinence from alcohol, cigarettes, and/or illicit substances, is one of the most highly effective and empirically supported treatments for substance use disorders. However, the financial cost of implementation has been identified as a major barrier to implementation of this treatment. The purpose of this study was to develop behavioral economic purchase tasks to assess interest in CM as a function of treatment cost and perceived effectiveness of CM as a function of abstinence incentive size in alcohol drinkers. Alcohol drinkers recruited from Amazon Mechanical Turk (MTurk) completed behavioral economic purchase tasks measuring demand for CM based on targeted abstinence intervals and treatment effectiveness and alcohol use disorder severity assessments. Nonlinear mixed-effects modeling was used to fit demand curves and assess the relationship between individual characteristics and demand metrics for CM. Results reveal that participants reported higher probability of remaining abstinent from drinking when offered larger incentives and required larger incentives when duration of abstinence required to earn the incentive was increased. Additionally, willingness to pay for treatment increased as effectiveness of treatment increased. Abstinence interval and treatment effectiveness are important features to consider when developing effective CM for widespread use, as these variables affected participants' likelihood of being abstinent and their interest in treatment. Future work will validate these assessments with actual treatment outcomes and determine predictors of CM treatment effectiveness. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

A general model of demand and discounting.

A simulation using a two-commodity demand model, with time as the constraint, replicated the primary findings from delay discounting experiments and introduces explicit terms for time elasticity and cross-price substitution into the delay discounting paradigm. A two-commodity temporal demand equation based on Hursh and Silberberg (2008) and Hursh (2014) was used to emulate delay discounting experiments. The own-price and cross-price demand curves intersected and plotting those indifference points emulated the usual hyperbolic discount function for substitutes. Simulations examined delay discounting in relation to (a) time elasticity of demand, (b) substitution between the delayed and immediate alternatives, and (c) amplitude of demand for the delayed alternative. The simulated discount functions with substitutes were hyperbolic. The discount rate was a direct function of increasing time elasticity and substitutability of delayed alternative demand, shifting the function toward an exponential model. Amplitude of demand for the delayed alternative was inversely proportional to discount rate and supported a hyperboloid model with a power function of time (Killeen, 2015; Rachlin, 2006). The emulation of cross-commodity discounting involving drugs points to amplitude and persistence of time-dependent demand and cross-commodity substitution as primary factors. This report describes the first general model of time-dependent demand and delay discounting. The model implicates cross-commodity substitution as a potential factor in delay discounting. In the context of substance use disorder, the model underscores the importance of defining the properties of multicommodity demand (time elasticity, substitution, and amplitude) specific to the commodities and context. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Estimating the Operating Reserve Demand Curve for Efficient Adoption of Renewable Sources in Korea

As the proportions of variable renewable sources (VRSs) such as solar and wind energy increase rapidly in the power system, their uncertainties inevitably undermine power supply reliability and increase the amount of operating reserve resources required to manage the system. However, because operating reserves have the characteristics of a public good and their value is related to the social cost of blackouts, it is difficult to determine their market price efficiently, which leads to inefficiencies in procuring operating reserves. This study estimates the operating reserve demand curve (ORDC) of the Korean power system to provide an effective basis for measuring the proper value and quantity of operating reserves needed to meet the reliability standard. A stochastic dynamic optimization model is applied to incorporate the probabilistic characteristics of VRS and the inter-hour constraint, which is necessary for analyzing load-following reserves. An econometric model and the Monte Carlo simulation method are used to generate the forecast profiles of solar and wind generation. The results indicate that the proper amount of hourly operating reserves needed in 2034 is approximately 4.4 times higher than that in 2020 at the current reserve offer price. The ORDC of 2020 has a price-inelastic shape, whereas the ORDC of 2034 has a price-elastic shape because the reserve requirement varies considerably with its offer price level in the high-VRS penetration case. This variability is due to alternatives, such as VRS curtailment or load shedding, which can replace the reserve requirement. This study also showed that VRS curtailment is an effective balancing resource as an alternative to reserves.

Regulated peer-to-peer energy markets for harnessing decentralized demand flexibility

Transactive energy systems use dynamic electricity markets to present an improved way to control large numbers of distributed energy resources while maintaining user privacy, autonomy and scalability. However, this access to dynamic electricity prices can introduce negative emergent behavior like high peak loads if not adequately regulated by grid operators. To solve this, this paper proposes a regulated peer-to-peer market structure for residential prosumers. Prosumers bid energy demand or supply curves into a peer-to-peer market, which can then be directly used by the utility to quantify and dispatch demand flexibility. A utility surcharge mechanism can then be employed to dispatch this flexibility, and achieve significant improvements to reliability. This framework is tested in a simulation environment of electrified residential buildings with significantly enhanced fidelity representing the real-world housing stock. New York State and the month of January was chosen for the demonstration purpose, considering that January is one of the most challenging months for fully electrified systems in cold climates as solar production occurs at opposite times of the highest heating load. Results show that the surcharge can reducing peak load and ramp rates by nearly 50%, while also encouraging consumption of locally generated electricity.

Microgrid system energy management with demand response program for clean and economical operation

A fundamental microgrid system's load demand often fluctuates hourly. Utilities establish different prices at various times based on the fluctuation of the load demand curve, this is referred to as electricity price based on time-of-use (TOU). The overall contribution of the paper is manifold which involves the techno-economic impacts of grid participation, electricity pricing and renewables. However, the primary goal is to offer a demand-response (DR) model that maximizes the benefits of energy retailers, in this case the microgrid customers. DR models examine the utility and elasticity of various customers, taking into account their different behaviors during both peak and valley periods. Considering that 40 % of the customers participate in the DR program, an exhaustive optimization process is initiated to calculate the optimal incentive value. Thereafter, a novel intelligent algorithm is implemented to minimize the overall cost of a microgrid system and analyze the outcome with and without DR program. The many cost factors taken into account include fuel costs, fined pollution costs, operating and maintenance costs, depreciation costs, etc. The use of a DR-based energy management microgrid system resulted in a significant decrease in overall generating costs from 880¥ to 872¥ along with pollutants released as compared to those described in the literature, according to numerical data. Furthermore, the peak demand was lowered by 5.13 % from 180 kW to 170.754 kW. The suggested optimization method is claimed to be superior by measures of central tendency analysis.

Impact on Voltage Regulation in Medium Voltage Distribution Networks Due to the Insertion of Photovoltaic Generators

The objective of this paper was to analyze the impacts caused by the operation of voltage regulators in electrical distribution networks and to evidence the number of operations in the face of short-duration voltage variation caused by the high intermittency of the connected PV generators. A real LV and MV feeder was used, modeled in OpenDSS software, based on normative standards, adjustments, and technical maneuvers strategically used by the local utility. The analyses considered the temporal variations for the photovoltaic generators and different load demand profiles connected to the feeder. The feeder was submitted to the demand curves varying the load percentage, framing it in high and conventional (nominal) load according to the profiles of consumers and prosumers connected. The simulations made it possible to observe the exacerbated elevation in the number of maneuvers performed by the voltage regulators of the network. The single-phase voltage regulators stood out by the elevation of control operations, causing premature wear of the PV generation equipment connected to the most loaded phase. It was observed that discrepancies in the power flow in the lines and the voltage levels at the busbars. The creation of strategies and decisions to correct these impacts caused to transformers and regulators is possible.

Impact of reduced nicotine content on behavioral economic measures of cigarette reinforcement in adolescents who smoke cigarettes

BackgroundPublic health officials in the U.S. and New Zealand are considering a reduced nicotine standard for cigarettes to reduce their addictive potential. This study’s aim was to evaluate the effects of nicotine reduction on the reinforcing efficacy of cigarettes in adolescents who smoke, which has implications for this policy’s potential for success. MethodsAdolescents who smoked cigarettes daily (n = 66; mean age: 18.6) participated in a randomized clinical trial assessing effects of assignment to very low nicotine content (VLNC; 0.4 mg/g nicotine) or normal nicotine content (NNC; 15.8 mg/g nicotine) cigarettes. Hypothetical cigarette purchase tasks were completed at baseline and the end of Week 3 and demand curves fit to the data. Linear regressions estimated effects of nicotine content on demand for study cigarettes at baseline and Week 3 and associations between baseline demand for cigarette consumption at Week 3. ResultsAn extra sum of squares F-test of fitted demand curves indicated that demand (α) was more elastic among VLNC participants at baseline and Week 3 (F(2, 1016)= 35.72, p < 0.001). Adjusted linear regressions indicated demand was more elastic (ß= 1.45, p < 0.01) and maximum expenditure (Omax) lower (ß= −1.42, p-0.03) among VLNC participants at Week 3. More elastic demand for study cigarettes at baseline predicted lower consumption of cigarettes at Week 3 (p’s < 0.01). ConclusionsA nicotine reduction policy may reduce the reinforcing value of combustible cigarettes among adolescents. Future work should investigate likely responses to such a policy among youth with other vulnerabilities and evaluate the potential for substitution to other nicotine containing products.

Comparison of Endoscopic and Minimally Invasive Transforaminal Lumbar Interbody Fusion for Lumbar Degenerative Diseases: A Meta-analysis.

Systematic review and meta-analysis. To compare the results of endoscopic transforaminal lumbar interbody fusion (Endo-TLIF) and minimally invasive transforaminal lumbar interbody fusion (MIS-TLIF) for patients with lumbar degenerative diseases. The MIS-TLIF has been widely used in lumbar degenerative diseases and achieved favorable clinical effects. The main disadvantage is the limited working space and visualization, especially in the deeper operational field, for preparing fusion bed. In recent years, with the development of surgical techniques, optical technology, and special instruments, Endo-TLIF has gradually been applied. A systematic review and meta-analysis of cohort studies between Endo-TLIF and MIS-TLIF in the lumbar degenerative diseases. The following outcome measures were extracted: visual analog scale (VAS), Oswestry Disability Index, fusion rate, disk height, segmental lordosis, operative time, length of hospital stay and complications. Data analysis was performed by RevMan 5.3. Eight studies comprising 687 patients were included in this meta-analysis. The pooled result revealed there was no significant differences in the VAS of leg, Oswestry Disability Index, fusion rate, disk height, segmental lordosis, and complication rate between the 2 groups ( P >0.05). However, the VAS of back in the Endo-TLIF group was significantly less than those in the MIS-TLIF group within 2 weeks after surgery [weighted mean difference (WMD)=-1.33 (-1.98, -0.68), P <0.0001] and at 3 months postoperatively [WMD=-0.72(-0.85, -0.59), P <0.00001]. The Endo-TLIF group also seemed to fewer VAS of back at the last follow-up (≥12mo) [WMD=-0.12 (-0.25, -0.00), P =0.05]. Compared with the MIS-TLIF group, the Endo-TLIF group was associated with longer operation time [WMD=26.74 (2.14, 51.34), P =0.03], but shorter length of hospital stay [WMD=-1.98(-2.91, -1.05), P <0.0001]. Compared with minimally invasive TLIF, endoscopic TLIF achieved comparable improvement of symptoms and intervertebral fusion, longer operation time, and smaller surgical trauma. Endoscopic TLIF, which requires a demanding learning curve, maybe a feasible and effective technique for the patients with symptomatic lumbar degenerative diseases.

Anhedonia modulates benzodiazepine and opioid demand among persons in treatment for opioid use disorder.

Benzodiazepine (BZD) misuse is a significant public health problem, particularly in conjunction with opioid use, due to increased risks of overdose and death. One putative mechanism underlying BZD misuse is affective dysregulation, via exaggerated negative affect (e.g., anxiety, depression, stress-reactivity) and/or impaired positive affect (anhedonia). Similar to other misused substances, BZD consumption is sensitive to price and individual differences. Although purchase tasks and demand curve analysis can shed light on determinants of substance use, few studies have examined BZD demand, nor factors related to demand. This ongoing study is examining simulated economic demand for alprazolam (among BZD lifetime misusers based on self-report and DSM-5 diagnosis; n = 23 total; 14 male, 9 female) and each participant's preferred-opioid/route using hypothetical purchase tasks among patients with opioid use disorder (n = 59 total; 38 male, 21 female) who are not clinically stable, i.e., defined as being early in treatment or in treatment longer but with recent substance use. Aims are to determine whether: (1) BZD misusers differ from never-misusers on preferred-opioid economic demand, affective dysregulation (using questionnaire and performance measures), insomnia/behavioral alertness, psychiatric diagnoses or medications, or urinalysis results; and (2) alprazolam demand among BZD misusers is related to affective dysregulation or other measures. Lifetime BZD misuse is significantly (p < 0.05) related to current major depressive disorder diagnosis, opioid-negative and methadone-negative urinalysis, higher trait anxiety, greater self-reported affective dysregulation, and younger age, but not preferred-opioid demand or insomnia/behavioral alertness. Alprazolam and opioid demand are each significantly positively related to higher anhedonia and, to a lesser extent, depression symptoms but no other measures of negative-affective dysregulation, psychiatric conditions or medications (including opioid agonist therapy or inpatient/outpatient treatment modality), or sleep-related problems. Anhedonia (positive-affective deficit) robustly predicted increased BZD and opioid demand; these factors could modulate treatment response. Routine assessment and effective treatment of anhedonia in populations with concurrent opioid and sedative use disorder may improve treatment outcomes. https://clinicaltrials.gov/ct2/show/NCT03696017, identifier NCT03696017.

Computational Simulation Model to Predict Behavior Changes in Inflammatory Bowel Disease Patients during the COVID-19 Pandemic: Analysis of Two Regional Japanese Populations.

Managing inflammatory bowel disease (IBD) is a major challenge for physicians and patients during the COVID-19 pandemic. To understand the impact of the pandemic on patient behaviors and disruptions in medical care, we used a combination of population-based modeling, system dynamics simulation, and linear optimization. Synthetic IBD populations in Tokyo and Hokkaido were created by localizing an existing US-based synthetic IBD population using data from the Ministry of Health, Labor, and Welfare in Japan. A clinical pathway of IBD-specific disease progression was constructed and calibrated using longitudinal claims data from JMDC Inc for patients with IBD before and during the COVID-19 pandemic. Key points considered for disruptions in patient behavior (demand) and medical care (supply) were diagnosis of new patients, clinic visits for new patients seeking care and diagnosed patients receiving continuous care, number of procedures, and the interval between procedures or biologic prescriptions. COVID-19 had a large initial impact and subsequent smaller impacts on demand and supply despite higher infection rates. Our population model (Behavior Predictor) and patient treatment simulation model (Demand Simulator) represent the dynamics of clinical care demand among patients with IBD in Japan, both in recapitulating historical demand curves and simulating future demand during disruption scenarios, such as pandemic, earthquake, and economic crisis.

Intelligent Demand Side Management for Exhaustive Techno-Economic Analysis of Microgrid System

In a typical microgrid (MG) structure, the requisite of load varies from hour to hour. On the basis of the rise and fall of the load demand curve, the power system utilities fix the rate of electric power at different times of the day. This process is known as time-of-usage (TOU)-based pricing of electricity. The hourly basis load demand can be categorized into elastic hourly load demand and inelastic hourly load demand. For the duration of the peak hours, when the utility charges more, the elastic loads are shifted to low demand hours by the demand side management (DSM) to save the cost. This rebuilds the total demand model on the pillars of demand price elasticity. Keeping in view the fact that the total load in an hour in an MG structure consists of 10% to 40% of elastic loads, the paper proposes an intelligence-technique-based DSM to achieve reduction in the overall cost of using loads in an MG structure. Seven different cases are studied which cover diverse grid participation and electricity market pricing strategies, including DSM programs. The results obtained for all the MGs showcase the applicability and appropriateness of using the proposed DSM strategy in terms of cost savings.

Application of the Gradient-Based Metaheuristic Optimizerto Solve the Optimal Conductor Selection Problemin Three-Phase Asymmetric Distribution Networks

This study addresses the problem of selecting the conductor sizes for medium-voltage distribution networks with radial configurations. The optimization model that represents this problem is part of the mixed-integer non-linear programming (MINLP) models, in which a power flow must be solved for each possible combination of conductor sizes. The main objective of this optimization problem is to find the best set of conductor sizes that minimize an economic objective function composed of the total costs of conducting materials added with the expected annual costs of the energy losses by proposing a new hybrid optimization methodology from the family of combinatorial optimization methods. To solve the MINLP model, a master–slave optimization method based on the modified version of the gradient-based metaheuristic optimizer (MGbMO) combined with the successive approximation power flow method for unbalanced distribution networks is presented. The MGbMO defines the set of conductor sizes assignable for each distribution line using an integer codification. The slave stage (three-phase power flow) quantifies the total power losses and their expected annual operating costs. Numerical results in the IEEE 8-, 27-, and 85-bus grids demonstrate the effectiveness of the proposed master–slave optimizer when compared with multiple combinatorial optimization methods (vortex search algorithm, the Newton-metaheuristic optimizer, the traditional and Chu and Beasley genetic algorithms, and the tabu search approaches). Two scenarios regarding the demand behavior were analyzed for the IEEE 8- and 27-bus grids: a peak load operation was considered, and, for the IEEE 85-bus grid, the daily demand behavior, including the presence of renewable generators, was considered. The 85-bus grid allowed showing that the most realistic operative scenario for selecting conductors is the case where a demand curve is implemented since reductions over 40% in the annual investment and operating costs were found when compared to the peak load operating condition. All numerical validations were performed in MATLAB software.

Residential electricity package model considering load difference penalty in China

The increasing load difference between peak and off-peak seasons has led to the unstable operation of China’s power systems in recent years. In addition, the current electricity package structure system of China, which is gradually liberalizing its electricity market, remains imperfect. This paper establishes a model of residential electricity packages that considers load difference penalty during peak and off-peak seasons. First, consumers’ utility of different packages is established and thus the optimal is determined by maximizing the utility function. Then, a mathematical model focusing on the minimization of load difference of retail electricity packages during peak and off-peak seasons is established. We thus design a particle-swarm based algorithm to solve the problem. Through simulation results of several numerical examples, we demonstrate that the profit of retailer is increased by 37.35% and Load difference during peak and off-peak seasons is reduced by 78.01%, improving the power demand curve of users and the operation efficiency of power systems.