Conventional Demand Research Articles

Substitutability between organic and conventional poultry products and organic price premiums

The organic food sector is one of the most rapidly growing sectors in the food market. Organic price premiums and patterns of substitution between related organic and conventional products are important for organic products certification and market entry and expansion decisions of organic producers. In this project we analyzed price differentials (premiums) between organic and conventional eggs and between three categories of organic and conventional chicken products: whole birds, boneless breasts and whole legs. Farm level prices for delivery to first receivers for the period January 2004 and December 2013 show that, in absolute terms, the highest average organic premium is commanded by boneless skinless breast meat followed by organic eggs. We also estimated the demand functions for conventional eggs and chickens where the presence of organic prices as the right-hand-side variables enable us to obtain cross-price elasticities of conventional demands with respect to organic prices. In line with the extant literature, our results show highly inelastic and statistically significant own price effects for both conventional chickens (elasticity equals -0.13) and conventional eggs (elasticity equals -0.139). The cross-price elasticities of conventional chicken meat demand with respect to the price of organic chickens and the cross-price elasticity of conventional eggs demand with respect to organic egg price, which are rarely found in the literature, confirm the intuitively expected result that the conventional and organic varieties of the same product are substitutes. The estimated cross-price elasticity of conventional chicken meat demand with respect to organic chicken price equals 0.153 and the cross-price elasticity of conventional eggs demand with respect to organic eggs price equals 0.679.

A sustainable supply chain for organic, conventional agro-food products: The role of demand substitution, climate change and public health

Abstract In today's marketplace, conventional agricultural products are produced all around the world due to economic reasons. However, recent environmental challenges such as global warming and climate change has strongly been affected by the high volumes of chemical pesticides and fertilizers used to produce these products. Several published reports have indicated the negative effects of the chemicals used in conventional agriculture as well as the effect of climate change on human health. The aim of this study is to develop and analyze a multi-objective linear mathematical model for a sustainable supply chain with an agro-food deteriorating product. The product is produced by two different methods, both by organic and conventional means. In this way, we attempt to create a balance between the production and consumption of conventional and organic products in order to achieve three objectives: reducing costs, reducing environmental degradation, and increasing levels of consumer health. Partial backorder for each product, as well as conventional demand substitution by organic products, are taken into account. In order to assess the impact of supply chain operations on public health, we define a new function based on each product's production and consumption amounts as well as the amount of wastes. The developed model is solved by augmented ɛ-constraint method. Numerical results indicate the important role of supply chain's managers in the formation of sustainable health production and consumption patterns.

Implementation of a new network equilibrium model of travel choices

Abstract This paper develops a new combined network equilibrium model by using more behaviorally sound mathematical forms to represent the four travel choices (i.e., trip frequency, destination, mode, and route) in a conventional travel demand forecasting process. Trip frequency choice relates to the traveler decision on “making a trip” or “not making a trip” so it is given by a binary logit model. Destination choice is formulated as a parameterized dogit model of which the captivity parameters (expressed as functions of independent variables) allow individual travelers to be captive to specific destinations. Mode choice is given by a two-level nested logit model to avoid IIA restriction. Trip assignment is based on Wardrop's “user-optimized” principle. All model forms describing travel choices are in response to the level of services incurred by the transportation system. Through the introduction of inclusive values, the traveler decisions concerning trip frequency, destination, mode, and route choices are inherently interrelated and jointly determined. To obtain solutions of the new combined model, it was reformulated as an equivalent convex programming problem with linear constraints, a great advantage from the computational aspects. The model was applied empirically to a transportation network in New Jersey. The application results show that the new model is consistently better than the commonly used logit combined model in reproducing the observed trip flows from origin zones, origin to destination (O-D) trip flows, O-D trip flows by mode, and trip flows on the network links.

Stochastic travel demand estimation: Improving network identifiability using multi-day observation sets

Stochastic travel demand estimation is essential to support many resilience and reliability based transportation network analyses. The problem of estimating travel demand based on sensor data often results in an ill-posed inverse problem, where solution uniqueness cannot be ensured. To overcome this challenge, effective utilization of more information/data, preferably from reliable sources, becomes critical. Conventional demand estimation methods often sacrifice system structural information during the process of compressing sensor data into its statistics. Loss of structural information, which captures critical relation between observed and estimated parameters, inevitably causes more dependence on unrealistic assumptions and unreliable data. Our model is designed to preserve all structural information contained from different observation sets and allow it to directly contribute to the identification of population parameters of travel demand. The proposed hierarchical framework integrates two traditionally distinctive identification problems, mean demand estimation and trip table reconstruction. Through mathematical analyses and numerical experiments, we show that the proposed framework improves parameter identifiability and leads to better estimation quality compared to conventional methods. The proposed framework is also flexible to accommodate a wide variety of travel behavior assumptions and estimation principles. As an example among many possible alternatives, Wardrop equilibrium based traffic assignment and generalized least square are implemented and tested using a case study based on a moderately large network.

A fundamental analysis on the implementation and development of virtual natural gas hubs

The ongoing gas market liberalization in Europe has brought up a new competitive environment in which shippers (i.e., companies that are responsible for conveying gas from producers to consumers) must adapt their behavior to the changing conditions. The development of gas virtual hubs increases market interactions among shippers, but the oligopolistic market structure may give room for strategic behavior. The market is in addition segmented by type of costumer. Each shipper maximizes its profit by supplying gas to households, businesses and industries (conventional costumers), participating in the electricity market, trading in the global LNG spot market and interacting with the rest of shippers in a virtual hub. During the hub implementation and development, the following questions arise: How do shippers behave at the different levels of hub maturity? And, to what extent does the implementation of virtual hubs in entry-exit systems diminish the barriers to entry of new market players, provides more flexibility and fosters competition?With this aim, the decision-making process of the different shippers is simulated under different market structures, representing four stages of the market liberalization process at different levels of hub maturity. First, the proto-liberalization case includes the global LNG spot market which is represented as a perfectly competitive market, the electricity market which is represented as an oligopoly, and the conventional demand which is assumed to be captive (i.e., monopolized). Second, a hub is implemented, which provides transparency and reduces information costs by revealing the gas price. Third, switching rates are expected to grow as consumers have access to a transparent gas price; hence, the conventional demand is no longer considered as captive. Fourth, wholesale (procurement) and retail activities are unbundled, and a wholesale market is established where the retailers presumably buy gas from the shippers; thereby wholesale and retail activities acquire importance and market transactions (i.e., liquidity) increase. From the simulation and the analysis of the different market equilibria, the following conclusions emerge. First, with the introduction of the virtual hub, the marginal cost of all shippers reaches a unique value, i.e., the transparent gas hub price. Second, the aggregated profit of the shippers is increasing even when anticompetitive behavior is not explicitly represented, due to the flexibility gained by shippers with the hub. Accordingly, and third, the hub is a necessary, but not sufficient, condition to increase competition. The entry of new players is critical and discouraging market regulations or the anticompetitive behavior of a highly concentrated market may not facilitate it.

Modelling Trip Distribution Using Fuzzy Logic Approach

Fuzzy logic is a soft computing technique, used for handling subjective, ambiguous and imprecise data. The decision to travel and choice of destination resulted from human are affected by socio-economic characteristics and residential locations and places of urban activities of the commuters. The subjective pattern of decision makers can be captured using a fuzzy logic system. The present study focuses the second stage of conventional travel demand modelling i.e. Trip distribution. Fast developing metropolitan city, Surat in South Gujarat has been considered as the study area. Southwest zone administratively delineated by Surat Municipal Corporation is selected as a typical subject area covering nearly 3.69 lakhs population to demonstrate model building on Fuzzy Logic. Subzones eight in number form the trip distribution matrix for the area in developing trip distribution model. The sub-zonal productions, attractions and the distance between the sub-zones are the major inputs for the Fuzzy Logic Trip Distribution Model (FLoTDM). MATLAB FIS toolbox is used for developing the model. The fuzzy model results are compared with the outputs of the traditional Gravity model. Results show that the Fuzzy Logic Trip Distribution Model is giving lesser error compared to the conventional Gravity Model. Hence, Fuzzy Logic approach can be used as a non-traditional trip distribution model.

A Heuristic Approach for Quantifying Household Travel GHG Emissions Using GPS Survey and Spatial Correlations-The Cincinnati Case Study

Household travel related Greenhouse Gas (GHG) emissions have been identified as one of the major contributors to greenhouse gas emissions. Many studies have suggested that household trips and their associated GHG footprints are pertinent in great part to land use type and socioeconomic of the household. The current practice of GHGs emission laws and regulations recommend using outputs from travel demand model for GHG and other regulated emission analysis. Conventional travel demand forecasting models are aimed at conducting a macroscopic simulation analysis at an area or regional level of the roadway network but it is unable to generate traffic flow operational data at a microscopic level such as speed, acceleration or deceleration at a fine spatiotemporal scale. On the other hand, the household travel GHG emissions, similar to the household location itself, are spatially and temporally dependent. The spatial factors’ role in the modeling of the household travel GHG footprint is unclear. To address the above gaps, this research proposes a robust household travel GHG quantification method with spatial information considered. By utilizing the greater Cincinnati GPS household travel survey data, household travel is accurately mapped to its origin and linked to the household’s socio-economics and demographic characteristics. The regional traffic analysis zone-based GHG emissions generated from the sampled households are, therefore, spatially modeled by using spatial regression models that originated from econometrics. The results showed that the Spatial Durbin Error model fits the data better comparing to other candidate models.

Random access schemes for satellite networks, from VSAT to M2M: a survey

SummaryIn this survey paper, we review the random access (RA) techniques with particular emphasis on the issues and the possible solutions applicable to satellite networks. RA dates back to the 1970s when the ALOHA protocol was developed to solve the problem of interconnecting university computers located in different Hawaiian islands. Since then, several evolutions of the ALOHA protocol have been developed. In particular, solutions were devised to mitigate the problem of packet collisions severely degrading the RA protocols performance. The approach followed for many years has been to avoid the occurrence of collisions rather than solving them. More recently, techniques tackling the RA packet collision problem have appeared triggered by the need of improving RA performance in satellite and terrestrial wireless networks. In particular, satellite networks large propagation delay does not allow the adoption of enhanced terrestrial RA techniques based on channel sensing. Adopting conventional demand assignment multiple access protocols is not suitable for supporting a large number of sensors or devices transmitting small‐size low duty cycle packets as required for machine‐to‐machine communications. This provided the stimulus to exploit successive interference cancelation schemes to solve packet collision issues. The use of successive interference cancelation in RA is relatively new and has opened up a promising research area. We provide an extensive review of recent high‐performance RA techniques achieving more than three orders of magnitude throughput increase compared with the original ALOHA at low packet loss rate. In this survey, we cover both slotted and unslotted techniques. Finally, we review the use of RA in satellite systems and related standards including recent proposals for machine‐to‐machine applications. Copyright © 2016 John Wiley & Sons, Ltd.

Modelling the global maritime container network

Travel demand modelling has a long history going back to 50s when the conventional four-step modelling structure was introduced and developed for Chicago and Detroit, USA. However, the first travel demand models for freight movements were not developed until 40 years after the first model for passenger cars. Freight models are yet limited to studies looking at goods movements by truck and rail. This paper explores the effectiveness of the conventional travel demand modelling techniques for maritime container movements. Two approaches for modelling the movement of trade in the global maritime container network are discussed. The conventional methods of trip generation and distribution are applied in a sequential model, and compared against an alternative joint model methodology. Results show that the sequential methodology achieves high accuracy, while the joint methodology reveals more detailed trade relationships. Significant relationships are revealed, such as the varying influence of airports, the negative impact of coastline length and the impact of being an island, on containerised trade volumes. The findings of this paper provide a basis for modelling the container network from a transportation discipline viewpoint.

Motivations behind irrationality in the shipping asset management

Purpose The purpose of this paper is to investigate and clarify “irrationality” problem through the maritime industry practices and leading incentives behind common investors. Design/methodology/approach This paper includes a review of broader business and economics literature; review of shipping business practices and detection of institutional pathways and misleading mechanisms behind the irrational preferences; investigation of data (for some arguments); and introduction of a theoretical approach. Findings There are several industry practices and norms well established and followed by decision makers, which may cause and initiate illogical and irrational (long-run) preferences. Short-termism is an erroneous habit of common shipping investors, which is embedded and forced through traditional financial math (i.e. discounted cash flow), financial system (e.g. initial public offerings with high-frequency transactions, interest rate governance and asset valuation mechanism) or flawed contracting tradition (i.e. commission bias). Practical implications Both shipping business and financial institutions need to redesign their working mechanisms, evaluation systems, risk detection and assessment procedures. As discussed in Section 4.7, commission-based (float) services must be converted to regular flat rate payments with long-term contracts to protect investors from rational choices of intermediaries in the short-run which encourages investor’s irrationality. Having a long-term service contract will also improve sustainability of intermediaries and lower their business risk (win-win). Originality/value The impact of this paper is two-fold. First, it raises critical questions about professional decay and drawbacks of some traditional instruments in the shipping business. For the first time, this paper emphasises on various challenges which deteriorate credibility of the industry and causes ill-defined investments. Some arguments have extreme priority for strengthening the foundations of the industry. Second, this paper establishes a new stream of scholarly research highlighting weaknesses of conventional economic approach and demand for outsourcing other schools of economics (e.g. institutional and behavioural) into the shipping business.

Performance evaluation of conventional demand response at building-group-level under different electricity pricings

One main objective of demand response is to release grid pressures imposed by building peak demands through changing end-user electric usage in response to electricity price changes. Conventional demand response in heating, ventilation and air-conditioning (HVAC) systems is performed in an uncoordinated way which merely focuses on individual-building-level economic benefits. However, its building-group-level performance, which is the real concern to grid, has not been systematically evaluated. Thus, using operation data of real buildings, this study evaluates performance of conventional demand response at building-group-level under common electricity pricings. The evaluation results disclose major limitations of conventional demand response due to lack of coordination. Under time of use pricing, conventional HVAC demand response cannot effectively and efficiently reduce peak demand at building-group-level. Under dynamic pricing, conventional HVAC demand response can cause a new undesirable peak demand at building-group-level which could be much larger than the original one and impose stress on grid. Coordinating demand response of individual buildings can solve these limitations. With improved performance at building-group-level, simple coordinated examples have been given to demonstrate the need of coordination in conventional demand response. The study results show the significance of coordination in demand response and the grid pressures imposed by building peak demands can be better released if coordinated demand response implemented.

A combined model of trip distribution and route choice problem

ABSTRACTAlthough a close relationship exists between trip distribution and traffic assignment, a significant inconsistency lies in the relationship: a lack of process for feedback between trip distribution and traffic assignment in the conventional travel demand forecasting steps. This paper eliminates this inconsistency by combining the two models into a single feedback process within a single framework. In addition, the paper develops a solution algorithm for the combined model. The proposed model uses the gravity model for trip distribution and the logit model for route choice. As the user equilibrium for the route choice problem, the paper demonstrates the existence of an equilibrium condition for the proposed model. Further, the paper conducts a sensitivity analysis to assess the differences between the proposed model and existing combined models.

Anaerobic treatment to improve sludge recovery at a deinked fiber pulp and paper mill

Investigations were conducted to explore the use of anaerobic treatment options that may allow complete recovery of primary and waste activated sludge streams for incineration from a graphic paper mill with an integrated deinked pulp plant. The effective reduction of waste activated sludge quantity (55%–65%) was obtained by the anaerobic treatment of wastewater from the deinked pulp plant or by the anaerobic stabilization of the mill effluent from primary treatment, where chemical oxygen demand (COD) removal efficiency of 67.6% and 64.8%, and biogas potential of 440 and 496 m3/t CODremoved were achieved, respectively. The anaerobic treatment of wastewater and alkali hydrolysis of waste activated sludge decreased the dewatering properties of solids residue in treated effluent resulting in a negative impact on the sludge recovery energy balance. The addition of municipal wastewater to anaerobic treatment in the amount of 18 vol%, representing 10% of the total COD, increased the reduction of waste activated sludge to 78%, with a positive impact on the treatment costs and efficiency. Anaerobic treatment of the studied effluents may enable incineration of all mill sludge with a positive energy balance, a reduction of biological wastewater treatment operational cost by up to 83%, and mill conventional energy demands by up to 2.7%, resulting in total cost savings of up to EUR 5.2 (USD 5.60) per ton of paper.

An activity pattern – destination land use choice model of low income households of informal settlements – Case study of Delhi

Various transportation studies carried out in India, while estimating the travel demand, do not take into consideration the travel characteristics of different income groups. The conventional transportation travel demand model lacks the ability to address the travel needs of the urban poor. This paper explores the factors influencing the travel destinations of urban poor living in informal settlements and finds that travel times have a significant negative impact on the choice to travel and influences the choice of the destinations. The study also finds that the inhabitants of informal settlements are adversely affected by urban policies that displace them and rehabilitate them far from their employment opportunities and that the travel characteristics of low income households living in informal settlements are significantly different from higher income households.

Dealing with discomfort: how the unspeakable confounds wicked planning problems

ABSTRACTThe idea of ‘wicked’ problems has made a valuable contribution to recognising the complexity and challenges of contemporary planning. However, some wicked policy problems are further complicated by a significant moral, psychological, religious or cultural dimension. This is particularly the case for problems that possess strong elements of abjection and symbolic pollution and high degrees of psychosocial sensitivity. Because this affects the way these problems are framed and discussed they are also characterised by high levels of verbal proscription. As a result, they are not discussed in the rational and emotion-free way that conventional planning demands and can become obscured or inadequately acknowledged in planning processes. This further contributes to their wickedness and intractability. Through paradigmatic urban planning examples, we argue that placing their unspeakable nature at the forefront of enquiry will enable planners to advocate for a more contextually and culturally situated approach to planning, which accommodates both emotional and embodied talk alongside more technical policy contributions. Re-imagining wicked problems in this way has the potential to enhance policy and plan-making and to disrupt norms, expose their contingency, and open new ways of planning for both the unspeakable and the merely wicked.

Organic field trials to promote Hungarian organic cereal production – testing wheat varieties

Among various factors affecting the cultivation of organic cereals, the biological basis is one of the most important ones. Within organic arable cropping the cash crop grown on the largest surface in Hungary is common wheat ( Triticum aestivum ). 2012-2015 we conducted a series of participatory on-farm experiments involving a number of organic farmers from all over Hungary in order to identify best suited wheat varieties for Hungarian organic farming. We studied the most important standard quality measures (protein, gluten, falling number) and yields, which strongly affect the economy of organic cereal production. Results show that there is a gap between conventional breeding aims and demands in organic farming. Moreover, even farmers’ proven varieties cannot guaranty premium quality yield in climatically difficult years. There is strong need to continue variety testing and identification of well adapted cultivars. Keywords: organic wheat variety test, on-farm research, Hungary

Aggregated demand response modelling for future grid scenarios

With the increased penetration of intermittent renewable energy sources (RESs) in future grids (FGs), balancing between supply and demand will become more dependent on demand response (DR) and energy storage. Thus, FG feasibility studies will need to consider DR for modelling nett future demand. Against this backdrop, this paper proposes a demand model which integrates the aggregated effect of DR in a simplified representation of the effect of market/dispatch processes aiming at minimising the overall cost of supplying electrical energy. The conventional demand model in the optimisation formulation is augmented by including the aggregated effect of numerous users equipped with rooftop photovoltaic (PV)-storage systems. The proposed model is suited for system studies at higher voltage levels in which users are assumed to be price anticipators. As a case study, the effect of the demand model is studied on the load profile, balancing and loadability of the Australian National Electricity Market in 2020 with the increased penetration of RESs. The results are compared with the demand model in which users are assumed to be price takers.

A GA-based coordinated demand response control for building group level peak demand limiting with benefits to grid power balance

Demand response controls, i.e. efforts from the demand side, are widely considered as a good alternative to supply side management for helping maintain grid balance. Conventional demand response controls of commercial buildings are conducted in an uncoordinated way and they focus on minimizing individual building level peak demand. But the overall peak demand of a building group, which is the main concerns of a grid, is overlooked and cannot be effectively minimized. Therefore, this study proposes a genetic algorithm (GA) based demand response control which aims to minimize the building group level peak demand with energy efficiency. Case studies have been conducted to show the ineffectiveness and inefficiency of the conventional control in relieving grid pressure caused by the overall building peak demand. The study results also demonstrate the improved performance of the proposed control in terms of building group level peak demand limiting and the associated extra energy consumption.

Iterative transmission and distribution optimal power flow framework for enhanced utilisation of distributed resources

This study focuses on developing an integrated transmission and distribution framework that couples the two sub-systems together with due consideration to conventional demand flexibility. The proposed framework ensures accurate representation of the system resources and the network conditions when modelling the distribution system in the transmission optimal power flow and vice-versa. It is further used to develop an accurate pricing mechanism (distribution-based location marginal pricing), which is reflective of the moment-to-moment costs of generating and delivering electrical energy, for the distribution system. By accurately modelling the two sub-systems, the authors can improve the economic efficiency and the system reliability, as the price sensitive resources can be controlled to behave in a way that benefits the power system as a whole.

Islamic House Financing in Pakistan: A Demand Analysis

his paper analyzes the issue of the nature of demand for house financing offered by Islamic banks in the Pakistan. The paper also identifies the factors that impact demand for house financing offered by Islamic banks. The study used survey data based on convenient face to face interview of 243 respondents. The study regressed conventional demand for Islamic house financing on six regressors: Shariah compliance, convenience, bank features, product features, social influence and knowledge. The study reveals that the nature of demand for Islamic house financing is conventional. The results of the study also indicate that religious factor negatively affect the demand for Islamic house financing whereas all other factors affect it positively. The study will help managers of Islamic banks to increase their market share in house financing by focusing on those factors which significantly affect the demand for their product.