scholarly journals Consumer preferences for Certified Wines in France: A Comparison of Sustainable Labels

2021 ◽  
Author(s):  
Adeline Alonso Ugaglia ◽  
Britta Niklas ◽  
Wolfram Rinke ◽  
Jeff Gow ◽  
Daniel Moscovici
Keyword(s):  
Neural Network ◽  
Artificial Neural Network Model ◽  
Consumer Preferences ◽  
Wine Industry ◽  
Supply Side ◽  
Demand Side ◽  
Wine Production ◽  
Social Challenges ◽  
Lack Of Information ◽  
Willingness To Buy

The wine industry has faced various environmental and social challenges. On the demand side, consumer demand for sustainable wines has been increasing but, to date, it is unknown whether consumers perceive wine companies’ efforts to obtain sustainable development (SD) certifications and labels as being valuable or how they differentiate them. On the supply side, sustainable wine production is increasing but producers report a lack of information to engage and select their SD strategy. This article uses a logistic regression and an artificial neural network model to show how French consumers differentiate and value different SD labels (Organic, Biodynamic, Sustainable, Fairtrade, Natural). Results show that consumers’ willingness to buy and willingness to pay are influenced by the importance each consumer gives to the certification. For all other drivers, consumers differentiate between labels, highlighting the importance of comparison between and knowledge about each of them, thereby aiding producers in choosing an appropriate marketing strategy.

scholarly journals Development of Optimized Methodology for Improvement Domestic Energy Management

2019 ◽  
Vol 8 (4) ◽  
pp. 5288-5294
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Wind Speed ◽  
Energy Management ◽  
Electrical Energy ◽  
Supply Side ◽  
Demand Side ◽  
Load Schedule ◽  
The Cost ◽  
Daily Load

Electrical energy management (EEM) is an object that has proceeds appointed importance in the 21 th - century in order to its assistance to economic development and ecological ascertainment. “EEM” may be perfected on the supply side “(SS)” or demand side “(DS)”. On the supply side, “EEM” is cultivated when: There is an outgrowth desire “(demand requirement is higher than supply)”. “EEM” assists to suspend the design a resent generation station. On the “DS”, “EEM” is used to minimize the cost of electrical energy consumption and the interrelated forfeitures. The technique utilized for “EEM” is demand side load management that plan at ending valley filling, peak clipping and strategic preservation of electrical systems [1]. Seeming new inventions like “distributed generation (DG)”, “distributed storage (DS)” and “DSLM” will modify the method we use and generate energy. A smart grid (SG) is an electrical network that manages electricity demand in an unstoppable sustainable, reliable and economic manner. A smart grid uses smart net meters to overcome the sickliness of traditional electrical grid. “(DSM)” is a vital advantage of “(SG)” to progress power efficiency, minimize the peak average load and minimize the cost. From basic purposes of DSM is shifting load from peak hours to off-peak hours and reducing consumption during peak hours. Generally, a deregulated grid system is considered where the retailer purchases electricity from the electricity market to cover the end users’ energy need. In this research, Demand Side Management (DSM) techniques (load shifting and Peak clipping) are used to maximize the profit for Retailer Company by reducing total power demand pending peak demand periods and achieve an optimal daily load schedule using linear programming method and Genetic Algorithm. This method is performed on the 69-bus radial network. Also, a short term Artificial Neural Network technique is used to get forecasted wind speed, solar radiation and forecasted users load for date 15-Aug-2019. The neural network here uses an actual hourly load data, actual hourly wind speed and solar radiation data. Then the forecasted data is used in the optimization to get optimal daily load schedule to maximize the profit for Retailer Company. Then comparison between profit using linear programing and genetic algorithm are made. The optimized DSM succeeded to maximize the profits of the company.

scholarly journals Understanding the demand side and coordinating the supply side for connected goods and services

2010 ◽  
Vol 4 (1-2) ◽  
pp. 17-23
Author(s):  
Gro Ladegård ◽  
Eirik Romstad
Keyword(s):  
Consumer Preferences ◽  
Value Added ◽  
Supply Side ◽  
Demand Side ◽  
Coordination Networks ◽  
Goods And Services ◽  
New Information ◽  
Production Space ◽  
Human And Social Capital ◽  
Two Sides

This paper addresses the coordination and innovation issues needed for promoting value added at the rural and regional level. There are two sides to value added: the ability to meet consumer demand, and to identify least cost ways of supplying the demanded goods. Human and social capital plays an important role on both sides. At the municipality level the supply side issues are complex. First, because the production space has far more dimensions than for the single entrepreneur. Second, because the value of some goods and services produced depend on what other goods and services that is available. On the supply side networks are important to solve the coordination issues, while networks for identifying and understanding consumer preferences are important on the demand side. Participation in these two network types compete for the same scarce resource, the time of the inhabitants of a municipality. We address these issues in more detail. A major insight from our work is that in addition to the time conflict, innovation and new information may make it more difficult to maintain coordination networks.

Artificial Neural Network Model for Prediction of Latent Heat Flux over Bay of Bengal

2019 ◽  
Vol 7 (5) ◽  
pp. 901-905
Author(s):  
Kanchan. V. Shende ◽  
Vishal S. Shirsat ◽  
M. R. Ramesh Kumar ◽  
K.V. Kale
Keyword(s):  
Neural Network ◽  
Heat Flux ◽  
Artificial Neural Network ◽  
Latent Heat ◽  
Latent Heat Flux ◽  
Network Model ◽  
Neural Network Model ◽  
Bay Of Bengal ◽  
Artificial Neural Network Model ◽  
Artificial Neural

Application of a Tax to E-Commerce Deliveries in Barcelona

2021 ◽  
pp. 036119812110124
Author(s):  
Genís Majoral ◽  
Francesc Gasparín ◽  
Sergi Saurí
Keyword(s):  
Cost Benefit Analysis ◽  
Cost Benefit ◽  
Supply Side ◽  
Environmental Tax ◽  
Positive Outcomes ◽  
Demand Side ◽  
Benefit Analysis ◽  
Revenue Collection ◽  
Last Mile ◽  
The Impact

The number of e-commerce transactions is increasing worldwide. Deliveries of goods purchased online generate externalities throughout the whole supply chain and, particularly, the increasing concern about the last-mile distribution of goods. The escalating presence of vans in cities contributes to poor air quality, climate change, noise, and congestion. So far, the majority of solutions to address this issue are based on the supply side, such as electric vans, optimizing the routing and pick-up-points, and so forth. Even in other transport sectors, pricing solutions are well known, yet they have not been extended to e-commerce delivery. This paper aims to propose an environmental tax falling on the demand side and equaling the externalities from this activity. The analysis has been particularized for the case of Barcelona. A cost–benefit analysis to assess the impact of such a tax has been carried out. When revenue collection is reinvested in the logistics sector, and for subsidizing electric distribution vehicles, the results indicate that the levying of the tax can generate positive outcomes.

scholarly journals Techniques for Mitigating the Effects of Smoke Taint While Maintaining Quality in Wine Production: A Review

Molecules ◽  
2021 ◽  
Vol 26 (6) ◽  
pp. 1672
Author(s):  
Ysadora A. Mirabelli-Montan ◽  
Matteo Marangon ◽  
Antonio Graça ◽  
Christine M. Mayr Marangon ◽  
Kerry L. Wilkinson
Keyword(s):  
Global Perspective ◽  
Wine Industry ◽  
Wine Production ◽  
Specific Data ◽  
Main Research ◽  
Initial Severity ◽  
The Subject ◽  
Available Information

Smoke taint has become a prominent issue for the global wine industry as climate change continues to impact the length and extremity of fire seasons around the world. Although the issue has prompted a surge in research on the subject in recent years, no singular solution has yet been identified that is capable of maintaining the quality of wine made from smoke-affected grapes. In this review, we summarize the main research on smoke taint, the key discoveries, as well as the prevailing uncertainties. We also examine methods for mitigating smoke taint in the vineyard, in the winery, and post production. We assess the effectiveness of remediation methods (proposed and actual) based on available research. Our findings are in agreement with previous studies, suggesting that the most viable remedies for smoke taint are still the commercially available activated carbon fining and reverse osmosis treatments, but that the quality of the final treated wines is fundamentally dependent on the initial severity of the taint. In this review, suggestions for future studies are introduced for improving our understanding of methods that have thus far only been preliminarily investigated. We select regions that have already been subjected to severe wildfires, and therefore subjected to smoke taint (particularly Australia and California) as a case study to inform other wine-producing countries that will likely be impacted in the future and suggest specific data collection and policy implementation actions that should be taken, even in countries that have not yet been impacted by smoke taint. Ultimately, we streamline the available information on the topic of smoke taint, apply it to a global perspective that considers the various stakeholders involved, and provide a launching point for further research on the topic.

scholarly journals A feedforward artificial neural network model for classification and detection of type 2 diabetes

2021 ◽  
Vol 1734 ◽  
pp. 012026
Author(s):  
Enoch A. Frimpong ◽  
Ariyo Oluwasanmi ◽  
Edward Y. Baagyere ◽  
Qin Zhiguang
Keyword(s):  
Neural Network ◽  
Type 2 Diabetes ◽  
Artificial Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Artificial Neural
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