Disaggregated Approaches to Freight Analysis: A Feasibility Study.

Forecasting the demand for freight transport is notoriously difficult. Although ever more advanced modelling techniques are becoming available, there is little data available for calibration. Compared to passenger travel, there are many fewer decision makers in freight, especially for the main bulk commodities, so the decisions of a relatively small number of principal players greatly influence the outcome. Moreover, freight comes in various shapes, sizes and physical states, which require different handling methods and suit the various modes (and sub-modes) of transport differently. In the face of these difficulties, present DTp practice is to forecast Britain's freight traffic using a very simple aggregate approach which assumes that tonne kilometres will rise in proportion to GDP. Although this simple model fits historical data quite well, there is a clear danger that this relationship will not hold good in the future. The relationship between tonne kilometres and GDP depends on the mix of products produced, their value to weight ratios, number of times lifted and lengths of haul. In the past, a declining ratio of tonnes to GDP has been offset by increasing lengths of haul. This has come about through a complicated set of changes in product mix, industrial structure and distribution systems. A more disaggregate approach which studies changes in all these factors by industrial sector seems likely to provide a better understanding of the relationship between tonne kilometres and GDP. However, there are also problems with disaggregation. As we disaggregate we get more understanding of what might change in the future, but are less able to project trends forward. This can be seen if we consider the future amounts of coal movements. Theoretically there is clearly scope for better forecasting by allowing for past trends to be overturned by a movement towards gas powered electricity generation and more imports of coal direct to coastal power stations. However, making such a sectoral forecast is extremely difficult, and inaccuracy here may more than offset the theoretical gain referred to earlier. This is because it is usually easier to forecast to a given percentage accuracy an aggregate rather than its components. For example, the percentage error on sales forecasts of Hotpoint washing machines will be greater than that for the sales of all washing machines taken together. This occurs because different makes of washing machines are substitutes for each other, so forecasts for Hotpoint washing machines must take into account uncertainty over Hotpoint's market share as well as uncertainty over the future total sales of washing machines. Nevertheless, a disaggregate investigation of the market could spot trends which were `buried' in the aggregate figures. For example, rapidly declining sales for one manufacturer might indicate their leaving the market, which with less competition would then price up and so reduce the total future sales. We have assumed above that the use of the term disaggregate in the brief refers to disaggregation by industrial sector. An alternative usage of the word disaggregate in this context is when referring to modelling at the level of the individual decision making unit. Disaggregate freight modelling in this sense would involve analysing decisions in order to determine the utility weight attached to different attributes of available transport options. Because data on suitable decisions is not readily available in this country, due to commercial confidentiality, we have recently undertaken research in which we have presented decision makers with hypothetical choices, and obtained the necessary utility weights from their responses. Whilst initial scepticism is understandable, this method has produced results acceptable for use in major projects. ITS itself has provided algorithms (known as Leeds Adaptive Stated Preference) which have been used to derive utility weights for use by British Rail in forecasting cross-channel freight, by DTp in evaluating the reaction of commercial vehicles to toll roads, and by the Dutch Ministry of Transport in modelling freight in the Netherlands. In the light of the above, the following objectives were set for the feasibility study: (1)To determine if a forecasting approach disaggregated by industrial sectors, as under the first definition above, can be used to explain recent trends in freight transport; (2)To test the feasibility of the disaggregated approach for improving the understanding of likely future developments in freight markets, this being informed by current best understanding of the disaggregate decision-making process as under the second definition above

A game-theoretic optimisation approach to fair customer allocation in oligopolies

Under the ever-increasing capital intensive environment that contemporary process industries face, oligopolies begin to form in mature markets where a small number of companies regulate and serve the customer base. Strategic and operational decisions are highly dependent on the firms’ customer portfolio and conventional modelling approaches neglect the rational behaviour of the decision makers, with regards to the problem of customer allocation, by assuming either static competition or a leader-follower structure. In this article, we address the fair customer allocation within oligopolies by employing the Nash bargaining approach. The overall problem is formulated as mixed integer program with linear constraints and a nonlinear objective function which is further linearised following a separable programming approach. Case studies from the industrial liquid market highlight the importance and benefits of the proposed game theoretic approach

Market power and long-term gas contracts: the case of Gazprom in Central and Eastern European Gas Markets

We explore a major European competition decision, the 2012-18 Gazprom case, using a global gas market simulation model. We find that access to LNG markets alone is insufficient to counterbalance Gazprom’s strategic behaviour; central and eastern Europe (CEE) needs to be well interconnected with bidirectional flow capability. ‘Swap deals’ created by the decision facilitate CEE market integration, while limiting Gazprom’s potential market power. Such deals may increase the diversity of contracted gas and number of market players, but do not improve physical supply diversity. In the next five years, swap deals could marginally impact negatively the utilization of strategic assets in CEE, but since Gazprom’s commitments expire by mid-2026, utilization of these strategic assets may fall considerably, especially if Gazprom withholds supplies. As an unintended consequence, CEE markets may disintegrate from the rest of Europe. Avoiding such outcomes will require further gas market reforms, particularly, market design for gas transportation

Socioeconomic Impacts of Long-Term Renewable Electricity Generation: a Multi-regional Analysis for Brazil

This thesis contributes to long-term renewable energy policymaking in developing economies by quantifying the net multi-regional macroeconomic, sectoral, and distributional impacts of renewable electricity investment in the case of Brazil from 2020 to 2050. Brazil has an outstanding potential for renewable electricity generation concentrated in its least developed region, the Northeast. New wind and solar power plants are currently channelling unprecedented investments to the Northeast, which should continue in the long run to maintain the low-carbon profile of electricity generation, potentially creating positive socioeconomic impacts and reducing regional inequalities. This thesis developed a recursive-dynamic Computable General Equilibrium (CGE) model called TERM-BR E15, which has representations of Brazil’s five official geoeconomic regions, nine electricity generation sources, ten household income bands and ten wage levels. The CGE model simulations consist of soft links with three energy-system models which provided two long-term renewable electricity policy scenarios and a baseline. Additionally, two industrial strategy options were simulated. Modelling results were tested against the policymaking process through an expert elicitation in which 13 senior-level institutions’ representatives of the sector in Brazil provided their insights. Results indicate that the more solar and wind power installed capacity in 2050, the more socioeconomic benefits to Brazil’s Northeast region, suggesting that a long-term renewable pathway is not only technically feasible, but also economically and socially beneficial. Regional GDP gains in the Northeast would be between 1.91% and 4.98% relative to the baseline in policy scenarios. All socioeconomic variables analysed indicate gains to the Northeast and reduced regional inequalities. Regional industrial policy in the Northeast yields more positive national results than incentives to specific components nationally, while developing the Northeast economy even further through new manufacturing segments. Socioeconomic development, however, entails structural change in various aspects beyond the scope of modelling that require multi-objective policies across government levels and departments

Pricing of Liqueed Petroleum Gas in North-West Europe

Liquefied Petroleum Gas (LPG) is a flammable mixture of hydrocarbon gases, mainly propane and butane, used for various heating purposes and as vehicle fuel. This thesis focuses on examining the LPG market, evaluating a couple of driving factor hypotheses for the propane price and developing a model for forecasting of future propane prices in North-West Europe, both on daily and monthly horizons. It is shown that especially two factors, crude oil (brent) and naphtha, which is a light crude oil distillate produced when refining crude oil, have strong relation to propane and may affect the propane price. Unfortunately, no good model to forecast the daily propane price was developed. For the monthly average price, the proposed models perform badly when forecasting the actual price, but one of the models, an AR(12)model, which can forecast the direction of propane price movements one and two months forward in time is presented. The AR(12) model is able to forecast the correct price movement direction with an accuracy of over 70%. This result is good, and shows that the AR(12) model is a useful tool in the LPG trading

Planning and Scheduling Optimization

Although planning and scheduling optimization have been explored in the literature for many years now, it still remains a hot topic in the current scientific research. The changing market trends, globalization, technical and technological progress, and sustainability considerations make it necessary to deal with new optimization challenges in modern manufacturing, engineering, and healthcare systems. This book provides an overview of the recent advances in different areas connected with operations research models and other applications of intelligent computing techniques used for planning and scheduling optimization. The wide range of theoretical and practical research findings reported in this book confirms that the planning and scheduling problem is a complex issue that is present in different industrial sectors and organizations and opens promising and dynamic perspectives of research and development

GTAP-E: An Energy-Environmental Version of the GTAP Model

Energy is an important commodity in many economic activities. Its usage affects the environment via CO2 emissions and the Greenhouse Effect. Modeling the energy-economy-environment-trade linkages is an important objective in applied economic policy analysis. Previously, however, the modeling of these linkages in GTAP has been incomplete. This is because energy substitution, a key factor in this chain of linkages, is absent from the standard model specification. This technical paper remedies this deficiency by incorporating energy substitution into the standard GTAP model. It begins by first reviewing some of the existing approaches to this problem in contemporary CGE models. It then suggests an approach for GTAP which incorporates some of these desirable features of energy substitution. The approach is implemented as an extended version of the GTAP model called GTAP-E, which includes the standard GTAP model as a special case. In addition, GTAP-E incorporates carbon emissions from the combustion of fossil fuels and this revised version of GTAP-E provides for a mechanism to trade these emissions internationally. The resulting behavior of agents in the model is analyzed using general equilibrium demand elasticities which summarize the combined effect of the new model specification. Implications for policy analysis are demonstrated via a simple simulation experiment in which global carbon emissions are reduced via a carbon tax. Results show that incorporating energy substitution into GTAP is essential for conducting analysis of this problem. The policy relevance of GTAP-E in the context of the existing debate about climate change is illustrated by some simulations of the implementation of the Kyoto Protocol. It is hoped that the proposed model will be used by individuals in the GTAP network who may not be themselves energy modelers, but who require a better representation of the energy-economy linkages than is currently offered in the standard GTAP model