Análise de concorrência no mercado mundial de café verde

A literatura argumenta que o Brasil, embora ainda seja o maior exportador mundial de café verde, tem perdido poder neste mercado, pois a concorrência (rivalidade e probabilidade de entrada) imposta por países como a Colômbia e o Vietnã é forte o suficiente para tornar este mercado bastante competitivo. Assim, este artigo avalia o padrão recente de concorrência do mercado mundial de café verde utilizando uma metodologia econométrica mais usualmente empregada em análise antitruste. Para avaliar o comportamento dos consumidores, foram estimadas as elasticidades-preço da demanda mundial de café verde, por tipo de café, usando o modelo de demanda Logit Multinomial Antitruste. Para avaliar o comportamento de equilíbrio de mercado foram realizados testes de instabilidade de share de quantidade por meio de análise de cointegração em painel. Os resultados apontam para aumento da concorrência à variedade de café brasileiro por parte da demanda e manutenção de sharede quantidades como configuração de equilíbrio de mercado.

Integrating Pricing and Inventory Control: Is it Worth the Effort?

In this paper we first show that the gains achievable by integrating pricing and inventory control are usually small for classical demand functions. We then introduce reference price models and demonstrate that for this class of demand functions the benefits of integration with inventory control are substantially increased due to the price dynamics. We also provide some analytical results for this more complex model. We thus conclude that integrated pricing/inventory models could repeat the success of revenue management in practice if reference price effects are included in the demand model and the properties of this new model are better understood.

Modelos de demanda potencial de viajeros en redes de transporte público : aplicaciones en el Metro de Madrid

Este trabajo propone una metodología basada en Sistemas de Información Geográfica para estimar la demanda de viajes en estaciones de redes de transporte público, tomando como ejemplo la red de metro de Madrid. Primero se emplea una serie de datos descriptivos para caracterizar la red, clasificar las estaciones y obtener una tipología de las mismas. Luego, con el objetivo de explicar y predecir los viajes (entradas a la red) se generan dos modelos: uno sencillo a partir de las tasas de penetración de uso del metro en función de la distancia (distance decay), y otro más complejo basado en un modelo de regresión lineal múltiple (MRLM) que incorpora variables relativas a la estación y su entorno (densidad, mezcla de usos, diseño urbano, presencia de modos competidores). Su aplicación muestra resultados alentadores, y se plantea como una alternativa a los clásicos modelos de cuatro etapas, más complejos y con un mayor coste económico.

Evaluating the equity effects of road-pricing in the European urban context - The Madrid Metropolitan Area

The paper identifies the potential spatial and social impacts of a proposed road-pricing scheme for different social groups in the Madrid Metropolitan Area (MMA). We appraise the accessibility of different districts within the MMA in terms of the actual and perceived cost of using the road infrastructure ‘before’ and ‘after’ implementation of the scheme. The appraisal framework was developed using quantitative survey data and qualitative focus group discussions with residents. We then simulated user behaviours (mode and route choice) based on the empirical evidence from a travel demand model for the MMA. The results from our simulation model demonstrated that implementation of the toll on the orbital metropolitan motorways (M40, M30, for example) decreases accessibility mostly in the districts where there are no viable public transport alternatives. Our specific study finding is that the economic burden of the road-pricing scheme particularly affects unskilled and lower income individuals living in the south of the MMA. The focus groups confirmed that low income drivers in the south part of the MMA would reduce their use of tolled roads and have to find new arrangements for these trips: i.e. switch to public transport, spend double the time travelling or stay at home. More generally, our research finds that European transport planners are still a long way from recognising the social equity implications of their policy decisions and that more thorough social appraisals are needed to avoid the social exclusion of low income populations when road tolling is proposed.

Evaluating the spatial and social equity effects of road pricing in the European urban context: the Madrid Metropolitan Area.

The paper explores the spatial and social impacts arising from implementation of a road-pricing scheme in the Madrid Metropolitan Area (MMA). Our analytical focus is on understanding the effects of the scheme on the transport accessibility of different social groups within the MMA. We define an evaluation framework to appraise the accessibility of different districts within the MMA in terms of the actual and perceived cost of using the road infrastructure "before" and "after" the implementation of the scheme. The framework was developed using quantitative survey data and qualitative data from focus group discussions with residents. We then simulated user behaviors (mode and route choice) based on the empirical evidence from a travel demand model for the MMA. The results from our simulation model demonstrated that implementation of the toll on the orbital metropolitan motorways (M40, M30, for example) decreases accessibility, mostly in the districts where there are no viable public transport alternatives. Our key finding is that the economic burden of the road-pricing scheme particularly affects unskilled and lower income individuals living in the south of the MMA. Consequently lower income people reduce their use of tolled roads and have to find new arrangements for these trips: i.e. switch to the public transport, spend double the time for their commuter trips or stay at home. The results of our research could be applicable more widely for anyone wishing to better understand the important relationship between increased transport cost and social equity, especially where there is an intention to introduce similar road-pricing schemes within the urban context.

Impact of weather dependency on planning decisions in the australian national electricity market

A long-term planning method for the electricity market is to simulate market operation into the future. Outputs from market simulation include indicators for transmission augmentation and new generation investment. A key input to market simulations is demand forecasts. For market simulation purposes, regional demand forecasts for each half-hour interval of the forecasting horizon are required, and they must accurately represent realistic demand profiles and interregional demand relationships. In this paper, a demand model is developed to accurately model these relationships. The effects of uncertainty in weather patterns and inherent correlations between regional demands on market simulation results are presented. This work signifies the advantages of probabilistic modeling of demand levels when making market-based planning decisions.

Integrated assessment of water conservation practices for sustainable management strategies

Miami-Dade County implemented a series of water conservation programs, which included rebate/exchange incentives to encourage the use of high efficiency aerators (AR), showerheads (SH), toilets (HET) and clothes washers (HEW), to respond to the environmental sustainability issue in urban areas. This study first used panel data analysis of water consumption to evaluate the performance and actual water savings of individual programs. Integrated water demand model has also been developed for incorporating property’s physical characteristics into the water consumption profiles. Life cycle assessment (with emphasis on end-use stage in water system) of water intense appliances was conducted to determine the environmental impacts brought by each practice. Approximately 6 to 10 % of water has been saved in the first and second year of implementation of high efficiency appliances, and with continuing savings in the third and fourth years. Water savings (gallons per household per day) for water efficiency appliances were observed at 28 (11.1%) for SH, 34.7 (13.3%) for HET, and 39.7 (14.5%) for HEW. Furthermore, the estimated contributions of high efficiency appliances for reducing water demand in the integrated water demand model were between 5 and 19% (highest in the AR program). Results indicated that adoption of more than one type of water efficiency appliance could significantly reduce residential water demand. For the sustainable water management strategies, the appropriate water conservation rate was projected to be 1 to 2 million gallons per day (MGD) through 2030. With 2 MGD of water savings, the estimated per capita water use (GPCD) could be reduced from approximately 140 to 122 GPCD. Additional efforts are needed to reduce the water demand to US EPA’s “Water Sense” conservation levels of 70 GPCD by 2030. Life cycle assessment results showed that environmental impacts (water and energy demands and greenhouse gas emissions) from end-use and demand phases are most significant within the water system, particularly due to water heating (73% for clothes washer and 93% for showerhead). Estimations of optimal lifespan for appliances (8 to 21 years) implied that earlier replacement with efficiency models is encouraged in order to minimize the environmental impacts brought by current practice.

Defining the Resolution of a Network for Transportation Analyses: a New Method to Improve Transportation Planning Decisions

Travel demand models are important tools used in the analysis of transportation plans, projects, and policies. The modeling results are useful for transportation planners making transportation decisions and for policy makers developing transportation policies. Defining the level of detail (i.e., the number of roads) of the transport network in consistency with the travel demand model’s zone system is crucial to the accuracy of modeling results. However, travel demand modelers have not had tools to determine how much detail is needed in a transport network for a travel demand model. This dissertation seeks to fill this knowledge gap by (1) providing methodology to define an appropriate level of detail for a transport network in a given travel demand model; (2) implementing this methodology in a travel demand model in the Baltimore area; and (3) identifying how this methodology improves the modeling accuracy. All analyses identify the spatial resolution of the transport network has great impacts on the modeling results. For example, when compared to the observed traffic data, a very detailed network underestimates traffic congestion in the Baltimore area, while a network developed by this dissertation provides a more accurate modeling result of the traffic conditions. Through the evaluation of the impacts a new transportation project has on both networks, the differences in their analysis results point out the importance of having an appropriate level of network detail for making improved planning decisions. The results corroborate a suggested guideline concerning the development of a transport network in consistency with the travel demand model’s zone system. To conclude this dissertation, limitations are identified in data sources and methodology, based on which a plan of future studies is laid out.

Train scheduling and rolling stock assignment in high speed trains

The Train Timetabling Problem (TTP) has been widely studied for freight and passenger rail systems. A lesser effort has been devoted to the study of high-speed rail systems. A modeling issue that has to be addressed is to model departure time choice of passengers on railway services. Passengers who use these systems attempt to travel at predetermined hours due to their daily life necessities (e.g., commuter trips). We incorporate all these features into TTP focusing on high-speed railway systems. We propose a Rail Scheduling and Rolling Stock (RSch-RS) model for timetable planning of high-speed railway systems. This model is composed of two essential elements: i) an infrastructure model for representing the railway network: it includes capacity constraints of the rail network and the Rolling-Stock constraints; and ii) a demand model that defines how the passengers choose the departure time. The resulting model is a mixed-integer programming model which objective function attempts to maximize the profit for the rail operator

Fuzzy clustering applied to a demand response model in a smart grid contingency scenario

This paper focus on a demand response model analysis in a smart grid context considering a contingency scenario. A fuzzy clustering technique is applied on the developed demand response model and an analysis is performed for the contingency scenario. Model considerations and architecture are described. The demand response developed model aims to support consumers decisions regarding their consumption needs and possible economic benefits.

Fuzzy clustering applied to a demand response model in a smatr grid contingency scenario

This paper focus on a demand response model analysis in a smart grid context considering a contingency scenario. A fuzzy clustering technique is applied on the developed demand response model and an analysis is performed for the contingency scenario. Model considerations and architecture are described. The demand response developed model aims to support consumers decisions regarding their consumption needs and possible economic benefits.

Flexibility and leadership advantages in a model with uncertain demand

We consider a differentiated Stackelberg model with demand uncertainty only for the first mover. We study the advantages of flexibility over leadership as the degree of the differentiation of the goods changes.

A multi-objective model for scheduling of short-term incentive-based demand response programs offered by electricity retailers

In this paper, we formulate the electricity retailers’ short-term decision-making problem in a liberalized retail market as a multi-objective optimization model. Retailers with light physical assets, such as generation and storage units in the distribution network, are considered. Following advances in smart grid technologies, electricity retailers are becoming able to employ incentive-based demand response (DR) programs in addition to their physical assets to effectively manage the risks of market price and load variations. In this model, the DR scheduling is performed simultaneously with the dispatch of generation and storage units. The ultimate goal is to find the optimal values of the hourly financial incentives offered to the end-users. The proposed model considers the capacity obligations imposed on retailers by the grid operator. The profit seeking retailer also has the objective to minimize the peak demand to avoid the high capacity charges in form of grid tariffs or penalties. The non-dominated sorting genetic algorithm II (NSGA-II) is used to solve the multi-objective problem. It is a fast and elitist multi-objective evolutionary algorithm. A case study is solved to illustrate the efficient performance of the proposed methodology. Simulation results show the effectiveness of the model for designing the incentive-based DR programs and indicate the efficiency of NSGA-II in solving the retailers’ multi-objective problem.

Regional Policy Spillovers: The National Impact of Demand-Side Policy in an Interregional Model of the UK Economy

UK regional policy has been advocated as a means of reducing regional disparities and stimulating national growth. However, there is limited understanding of the interregional and national effects of such a policy. This paper uses an interregional computable general equilibrium model to identify the national impact of a policy-induced regional demand shock under alternative labour market closures. Our simulation results suggest that regional policy operating solely on the demand side has significant national impacts. Furthermore, the effects on the non-target region are particularly sensitive to the treatment of the regional labour market.

Work-related stress according to the demand-control model and minor psychic disorders in nursing workers

This was a cross-sectional study that aimed to assess the association between work-related stress according to the Demand-Control Model, and the occurrence of Minor Psychic Disorder (MPD) in nursing workers. The participants were 335 professionals, out of which 245 were nursing technicians, aged predominantly between 20 and 40 years. Data were collected using the Job Stress Scale and the Self-Reporting Questionnaire-20. The analysis was performed using descriptive and analytical statistics. The prevalence of suspected MPD was 20.6%. Workers classified in the quadrants active job and high strain of the Demand-Control Model presented higher potential for developing MPD compared with those classified in the quadrant low strain. In conclusion, stress affects the mental health of workers and the aspects related to high psychological demands and high control still require further insight in order to understand their influence on the disease processes of nursing workers.