scholarly journals Direct and cross price elasticities of demand for gasoline, diesel, hybrid and battery electric cars: the case of Norway

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Lasse Fridstrøm ◽  
Vegard Østli
Keyword(s):  
Choice Model ◽  
Market Response ◽  
Energy Prices ◽  
Market Demand ◽  
Passenger Cars ◽  
Data Set ◽  
Electric Cars ◽  
Price Elasticities ◽  
Approval Rate ◽  
The Cross

Abstract Aim The primary goals of this research is (i) to derive direct and cross demand market response functions for automobile powertrains and their energy carriers and (ii) to assess how CO2 emissions from automobiles depend on vehicle and energy prices Methods The market demand for automobiles with differing powertrains is studied by means of a discrete choice model. Statistically precise coefficient estimates are calculated by means of a highly disaggregate data set consisting of virtually all 1.8 million new passenger car transactions in Norway during 2002–2016. Having estimated the model, we derive market response parameters in the form of direct and cross price elasticities of demand for gasoline, diesel, ordinary hybrid, plug-in hybrid and battery electric cars. Results The own-price elasticity of gasoline driven cars is estimated at −1.08, and those of diesel driven, battery electric and plug-in hybrid electric cars at –0.99, −1.27 and −1.72, respectively, as of 2016 in Norway. The cross price elasticities of demand for gasoline cars with respect to the price of diesel cars, and vice versa, are estimated at 0.64 and 0.51, while the cross price elasticities of demand for battery electric cars with respect to the prices of gasoline and diesel driven cars come out at 0.36 and 0.48, respectively. A 1 % increase in the price of liquid fuel in general is found to reduce the average type approval rate of CO2 emission from new passenger cars by an estimated 0.19%. Conclusion Fiscal policy measures affecting the prices of vehicles and fuel have a considerable potential for changing the long term composition of the vehicle fleet and its energy consumption, climate footprint and general environmental impact.

scholarly journals MNBDR: A Module Network Based Method for Drug Repositioning

Genes ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 25
Author(s):  
He-Gang Chen ◽  
Xiong-Hui Zhou
Keyword(s):  
Gene Expression ◽  
Protein Interactions ◽  
Drug Response ◽  
Drug Repositioning ◽  
Expression Profiles ◽  
Drug Repurposing ◽  
Recent Decade ◽  
Data Set ◽  
Module Network ◽  
The Cross

Drug repurposing/repositioning, which aims to find novel indications for existing drugs, contributes to reducing the time and cost for drug development. For the recent decade, gene expression profiles of drug stimulating samples have been successfully used in drug repurposing. However, most of the existing methods neglect the gene modules and the interactions among the modules, although the cross-talks among pathways are common in drug response. It is essential to develop a method that utilizes the cross-talks information to predict the reliable candidate associations. In this study, we developed MNBDR (Module Network Based Drug Repositioning), a novel method that based on module network to screen drugs. It integrated protein–protein interactions and gene expression profile of human, to predict drug candidates for diseases. Specifically, the MNBDR mined dense modules through protein–protein interaction (PPI) network and constructed a module network to reveal cross-talks among modules. Then, together with the module network, based on existing gene expression data set of drug stimulation samples and disease samples, we used random walk algorithms to capture essential modules in disease development and proposed a new indicator to screen potential drugs for a given disease. Results showed MNBDR could provide better performance than popular methods. Moreover, functional analysis of the essential modules in the network indicated our method could reveal biological mechanism in drug response.

The Interplay Between Online Reviews and Physician Demand: An Empirical Investigation

2021 ◽  
Author(s):  
Yuqian Xu ◽  
Mor Armony ◽  
Anindya Ghose
Keyword(s):  
Service Quality ◽  
Operations Management ◽  
Choice Model ◽  
Healthcare Delivery ◽  
Online Reviews ◽  
The United States ◽  
Patient Choice ◽  
Data Set ◽  
Textual Information ◽  
Time Service

Social media platforms for healthcare services are changing how patients choose physicians. The digitization of healthcare reviews has been providing additional information to patients when choosing their physicians. On the other hand, the growing online information introduces more uncertainty among providers regarding the expected future demand and how different service features can affect patient decisions. In this paper, we derive various service-quality proxies from online reviews and show that leveraging textual information can derive useful operational measures to better understand patient choices. To do so, we study a unique data set from one of the leading appointment-booking websites in the United States. We derive from the text reviews the seven most frequently mentioned topics among patients, namely, bedside manner, diagnosis accuracy, waiting time, service time, insurance process, physician knowledge, and office environment, and then incorporate these service features into a random-coefficient choice model to quantify the economic values of these service-quality proxies. By introducing quality proxies from text reviews, we find the predictive power of patient choice increases significantly, for example, a 6%–12% improvement measured by mean squared error for both in-sample and out-of-sample tests. In addition, our estimation results indicate that contextual description may better characterize users’ perceived quality than numerical ratings on the same service feature. Broadly speaking, this paper shows how to incorporate textual information into an econometric model to understand patient choice in healthcare delivery. Our interdisciplinary approach provides a framework that combines machine learning and structural modeling techniques to advance the literature in empirical operations management, information systems, and marketing. This paper was accepted by David Simchi-Levi, operations management.

Data Analysis on the Influencing Factors of the Real Estate Price

2021 ◽  
pp. 52-66
Author(s):  
Huang-Mei He ◽  
Yi Chen ◽  
Jia-Ying Xiao ◽  
Xue-Qing Chen ◽  
Zne-Jung Lee
Keyword(s):  
Data Analysis ◽  
Real Estate ◽  
Influencing Factors ◽  
Real Estate Market ◽  
Training Data ◽  
Market Demand ◽  
Data Set ◽  
The Real ◽  
The Real Estate ◽  
Real Estate Price

China has carried out a large number of real estate market reforms that change the real estate market demand considerably. At the same time, the real estate price has soared in some cities and has surpassed the spending power of many ordinary people. As the real estate price has received widespread attention from society, it is important to understand what factors affect the real estate price. Therefore, we propose a data analysis method for finding out the influencing factors of real estate prices. The method performs data cleaning and conversion on the used data first. To discretize the real estate price, we use the mean ± standard deviation (SD), mean ± 0.5 SD, and mean ± 2 SD of the price and divide it into three categories as the output variable. Then, we establish the decision tree and random forest model for six different situations for comparison. When the data set is divided into training data (70%) and testing data (30%), it has the highest testing accuracy. In addition, by observing the importance of each input variable, it is found that the main influencing factors of real estate price are cost, interior decoration, location, and status. The results suggest that both the real estate industry and buyers should pay attention to these factors to adjust or purchase real estate.

A Near‐Real‐Time Version of the Cross‐Calibrated Multiplatform (CCMP) Ocean Surface Wind Velocity Data Set

2019 ◽  
Vol 124 (10) ◽  
pp. 6997-7010
Author(s):  
Carl A. Mears ◽  
Joel Scott ◽  
Frank J. Wentz ◽  
Lucrezia Ricciardulli ◽  
S. Mark Leidner ◽  
...  
Keyword(s):  
Real Time ◽  
Wind Velocity ◽  
Surface Wind ◽  
Ocean Surface ◽  
Velocity Data ◽  
Data Set ◽  
Ocean Surface Wind ◽  
The Cross

scholarly journals A Household Production Analysis of Fuelwood Demand in Rhode Island

1986 ◽  
Vol 15 (1) ◽  
pp. 53-60 ◽  
Author(s):  
John MacKenzie ◽  
Thomas F. Weaver
Keyword(s):  
Rhode Island ◽  
Choice Model ◽  
System Modeling ◽  
Household Production ◽  
Equation System ◽  
Energy Prices ◽  
Cross Sectional Survey ◽  
Cross Sectional ◽  
The Relationship ◽  
Broad Policy

A model analyzing household substitution of fuelwood for other heating fuels is needed to clarify the relationship between energy prices and patterns of forest resource utilization. This paper employs the household production methodology to model fuelwood demand in Rhode Island. Data from a cross-sectional survey of 515 households are employed to test a discrete-choice model of household participation in wood-burning and a four-equation system modeling household production of heat and aesthetic benefits from fuelwood and stove capital. Control of selection bias via inclusion of an appropriate instrument allows analysis of aggregate demands. Some broad policy prescriptions applicable to the Northeast generally are presented.

scholarly journals Measurement of hadronic form factors at BESIII

2018 ◽  
Vol 172 ◽  
pp. 03002
Author(s):  
Haiming HU
Keyword(s):  
Form Factor ◽  
Cross Section ◽  
Center Of Mass ◽  
Polar Angle ◽  
Form Factors ◽  
Pion Form Factor ◽  
Angle Distribution ◽  
Data Set ◽  
Center Of Mass Energy ◽  
The Cross

The measurements of hadronic form factors of three modes using the data samples collected with the BESIII detector at BEPCII collider are presented. The cross section of e+e- → p p̅ at 12 energies from 2232.4 to 3671.0 MeV are measured, the electromagnetic form factor is deduced, and the ratio |GE/GM| is extracted by fitting the polar angle distribution. The preliminary results about the form factors of e+e- → ∧c+ ⊼c- will also be described. The cross section of e+e- → π+ π-between effective center-of-mass energy 600 and 900 MeV is measured by the ISR return method using the data set with the integrated luminosity of 2.93 fb-1 taken at ψ(3773) peak, the pion form factor is extracted.

scholarly journals Sensitivity Analysis in the Life-Cycle Assessment of Electric vs. Combustion Engine Cars under Approximate Real-World Conditions

2020 ◽  
Vol 12 (3) ◽  
pp. 1241 ◽  
Author(s):  
Eckard Helmers ◽  
Johannes Dietz ◽  
Martin Weiss
Keyword(s):  
Life Cycle Assessment ◽  
Life Cycle ◽  
Real World ◽  
Emission Inventory ◽  
Combustion Engine ◽  
Climate Impacts ◽  
Impact Categories ◽  
Passenger Cars ◽  
Electric Cars ◽  
On The Road

This study compares the environmental impacts of petrol, diesel, natural gas, and electric vehicles using a process-based attributional life cycle assessment (LCA) and the ReCiPe characterization method that captures 18 impact categories and the single score endpoints. Unlike common practice, we derive the cradle-to-grave inventories from an originally combustion engine VW Caddy that was disassembled and electrified in our laboratory, and its energy consumption was measured on the road. Ecoivent 2.2 and 3.0 emission inventories were contrasted exhibiting basically insignificant impact deviations. Ecoinvent 3.0 emission inventory for the diesel car was additionally updated with recent real-world close emission values and revealed strong increases over four midpoint impact categories, when matched with the standard Ecoinvent 3.0 emission inventory. Producing batteries with photovoltaic electricity instead of Chinese coal-based electricity decreases climate impacts of battery production by 69%. Break-even mileages for the electric VW Caddy to pass the combustion engine models under various conditions in terms of climate change impact ranged from 17,000 to 310,000 km. Break-even mileages, when contrasting the VW Caddy and a mini car (SMART), which was as well electrified, did not show systematic differences. Also, CO2-eq emissions in terms of passenger kilometers travelled (54–158 g CO2-eq/PKT) are fairly similar based on 1 person travelling in the mini car and 1.57 persons in the mid-sized car (VW Caddy). Additionally, under optimized conditions (battery production and use phase utilizing renewable electricity), the two electric cars can compete well in terms of CO2-eq emissions per passenger kilometer with other traffic modes (diesel bus, coach, trains) over lifetime. Only electric buses were found to have lower life cycle carbon emissions (27–52 g CO2-eq/PKT) than the two electric passenger cars.

scholarly journals Phase and Image Reconstruction from Pupil-Plane Observations in the Near Infrared: Integration of the Phasors

1994 ◽  
Vol 158 ◽  
pp. 197-200
Author(s):  
J.-L. Monin ◽  
N. Ageorges ◽  
L. Desbat ◽  
C. Perrier
Keyword(s):  
Image Reconstruction ◽  
Near Infrared ◽  
New Method ◽  
Data Set ◽  
Preliminary Results ◽  
Cross Spectrum ◽  
The Cross ◽  
Simulated Images ◽  
Pupil Plane

A new method to reconstruct the phase of bidimensional interferograms, obtained through pupil-plane interferometry is presented. We compute the average complex phasor components of the cross-spectrum on a data set to reconstruct the original unperturbed phase. We present preliminary results on simulated images which visibility phases are distorted using a model of atmospheric perturbed wavefronts.

New Results on Double Charmonium Production in e+e- Annihilation at $\sqrt{s} \approx 10.6~{\rm GeV}$ with the Belle Detector

2005 ◽  
Vol 20 (16) ◽  
pp. 3617-3620
Author(s):  
◽  
T. Ziegler
Keyword(s):  
Cross Section ◽  
Strong Evidence ◽  
Previous Analysis ◽  
Charmonium State ◽  
Data Set ◽  
Charmonium Production ◽  
Belle Detector ◽  
Theoretical Predictions ◽  
Order Of Magnitude ◽  
The Cross

Extending a previous analysis1 the double charmonium production [Formula: see text] and [Formula: see text] has been investigated with a data set of 155 fb-1 with the Belle detector. Theoretical predictions for the cross section are one order of magnitude lower than the measured value and this discrepancy is still not understood. In a very recent update with a dataset of 285 fb-1 strong evidence for a new charmonium state at a mass of 3.940 GeV was found.

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