Energy policy modelling in developing and industrializing countries

A large number of models have been developed and used for energy policy planning, in a regional, national or international level, in order to cope with the broad variety of issues related to the energy problem. Energy models belong to the class of policy models, which address fuzzy and complex issues involving many non-quantitative factors, such as political issues, behavioural aspects, etc., as well as many uncertainties and lack of rigorous knowledge concerning the structure of the reference system, and the interrelationships of its elements. The role of energy policy models is very important, since they enhance understanding and communication, and they assist the policymakers to review plausible future configurations of relevant decision variables and parameters. In this paper one of the most important areas of energy modelling is investigated, that concerning the interactions between energy and economy in the group of Developing and Industrializing Countries (DIC's). It is pointed out that energy models used in the DIC's must capture the particular features of energy policy in these countries, such as rapid economic development fueled by expensive, depleting and often imported energy resources; dependence on foreign resources such as energy, capital, technology, etc.; management of indigenous resources, social structural changes, rapid population growth, urbanization and industrialization. In order to improve energy models and enhance their contributions in policy analysis, it is proposed that modelling efforts should be directed towards a better understanding of the energy-economy relationships in the DIC's, as well as towards the development of validated data bases.     

Energy models for planning and policy assessment

The paper reviews energy models which have been applied to planning and policy assessment questions. It describes the history and methods of the early models, which had been developed for industrial purposes in the sixties. It illustrates how the scope of the model development and application changed during the seventies towards more universal applicable models of the total energy system and finally to models encompassing energy-economy interactions. The energy models applied today are mostly very large systems due to the complexity of the problems described. They consist sometimes of an integrated programming system, but in a few cases a set of disconnected models with different techniques are used.A general survey of energy models is given with the purpose to present the broad categories of some of the well-known models in comparative form, discuss their content and application and provide a classification. A selection of three models which are typical representatives for particular categories is reviewed in detail and then follows an outlook for areas of future improvements.     

Manufacturer's pricing strategy and return policy for a single-period commodity

This paper presents a model for designing the pricing and return-credit strategy for a monopolistic manufacturer of single-period commodities. That is, given the unit manufacturing cost and the unit retail sale price, the manufacturer determines: (i) the unit price C to be charged against the retailer; and (ii) the unit credit V to be given to the retailer for units returned. While the manufacturer is allowed to set C and V, the order quantity Q is set by the retailer in response to the manufacturer's C and V. Among the unexpected findings derived from our model are: (i) unless an external force supports the retailer, otherwise the manufacturer can usually design a (C,V)-scheme that gives himself the lion's share of the profit; (ii) depending on the risk attitudes of the manufacturer and the retailer, the optimal return policy can range from “no returns allowed” to “unlimited returns with full credit”; (iii) instead of losing his profit share to the retailer, a return-credits agreement can often be manipulated by a shrewd manufacturer to increase his profit.     

Innovation as a policy strategy for natural resource protection

Growing global food demands place major strains on water resources, including quality impairments and increased water scarcity. Drawing on the largely separate bodies of literature on externalities and technological innovation, this article develops a dynamic framework to explore the long‐term impacts of alternative policy approaches to the agricultural impacts on water resources. Environmental policies, which focus on correcting environmental externalities, lead to an overall gain because costs to farmers are more than offset by reduced environmental damages. Technology policies, which direct public investments into agricultural eco‐innovations, lead to benefits for farmers as well as the environment. Joint implementation of both types of policies leads to the largest overall gain. In principle, a technology policy alone could have greater environmental benefits than an environmental policy alone. This outcome is most likely in cases where the productivity effect of new technology is large and the cost of research is low. Recommendations for research managers

• As an alternative to traditional environmental policy, investments in research can provide win–win solutions that benefit the environment and agricultural producers.
• Conceivably, eco‐innovations could lead to environmental conditions that are better than those achieved by environmental policy alone.
• Adding research investments to existing environmental policy would lead to further improvements in environmental quality while also benefitting farmers.
• Unlike environmental policies that are perceived to impose costs on agriculture, technology policies impart benefits to farmers and are less likely to face political opposition from industry.
• Technology policies are likely to be the most effective when eco‐innovation leads to technologies that meaningfully reduce environmental impacts and also raise farm productivity.

     

Self-adaptive support vector machines: modelling and experiments

Method  In this paper, we introduce a bi-level optimization formulation for the model and feature selection problems of support vector machines (SVMs). A bi-level optimization model is proposed to select the best model, where the standard convex quadratic optimization problem of the SVM training is cast as a subproblem. Feasibility  The optimal objective value of the quadratic problem of SVMs is minimized over a feasible range of the kernel parameters at the master level of the bi-level model. Since the optimal objective value of the subproblem is a continuous function of the kernel parameters, through implicity defined over a certain region, the solution of this bi-level problem always exists. The problem of feature selection can be handled in a similar manner. Experiments and results  Two approaches for solving the bi-level problem of model and feature selection are considered as well. Experimental results show that the bi-level formulation provides a plausible tool for model selection.     

A multicriteria decision support tool for modelling bank credit ratings

We introduce an application of the SMAA-Fuzzy-FlowSort approach to the case of modelling bank credit ratings. Its stochastic nature allows for imprecisions and uncertainty that naturally surround a decision-making exercise to be embedded into the proposed framework, whilst its output complements the ordinal nature of a crisp classification with cardinal information that shows the degree of membership to each rating category. Combined with the SMAA variant of GAIA that offers a visual of a bank’s judgmental analysis, both recent approaches provide a holistic multicriteria decision support tool in the hands of a credit analyst and enable a rich inferential procedure to be conducted. To illustrate the assets of this framework, we provide a case study evaluating the credit risk of 55 EU banks according to their financial fundamentals.

     

Decision support models in climate policy

Climate change is considered among the most critical risks that global society faces in this century. So far, climate policy strategies have been evaluated by means of a variety of climate-economy models, or Integrated Assessment Models (IAMs), in the aim of supporting climate-related decision making. However, their inherent complexity, the number and nature of driving assumptions, and usual exclusion of stakeholders from the modelling process raise the issue of the extent to which they can provide fruitful insights for policy makers. Moreover, as with all modelling frameworks, IAMs inevitably fail to incorporate all relevant types of uncertainty and risk when used as stand-alone tools. This exclusion can have a significant impact on the model outcomes, but can be mitigated if experts’ knowledge is elicited in a structured manner and effectively taken into account, towards identifying such factors or reducing respective knowledge gaps. At the same time, a growing number of research publications have been suggesting decision support frameworks for assessing specific aspects in climate policy, based on “bottom-up” approaches and participatory processes. The objective of this paper is to provide a critical review of such frameworks—namely Portfolio Analysis, Multiple Criteria Decision Making and Fuzzy Cognitive Maps—in order to explore their strengths and weaknesses in this area, and propose a new integrative approach, appropriately exploiting blends of these frameworks, to productively complement IAMs, towards enhancing climate policy support.     

Integrating simulation modelling and GIS: spatial decision support systems for evacuation planning

A prototype spatial decision support system (SDSS) has been designed for contingency planning for emergency evacuations which combines simulation techniques with spatial data handling and display capabilities of a geographical information system (GIS). It links together the topographical support and analysis provided by the GIS–ARC/INFO, with a simulation model designed to simulate the dynamics of an evacuation process in detail. Our aim has been to design a SDSS so that it provides an interactive evacuation simulator with dynamic graphics that allows for experimentation with policies by providing rapid feedback from the simulation. The idea is that emergency planners will be able to use the SDSS to experiment with emergency evacuation plans in order to plan for different contingencies. This paper concentrates on the issues involved in designing an effective integration link interface between the GIS and the simulation model when building a SDSS of this type.     

Towards the integration of system modelling with scenario planning to support strategy: the case of the UK energy industry

A key aspect of strategy is sense-making of the unfolding uncertainty in the business environment and responding appropriately to achieve organisational objectives. However, uncertainty means that there is more than one future open to an organisation. Sense-making is therefore problematic. Scenario planning is one approach to sense-making that helps to explore and understand uncertainty, aiming for the identification of potential predetermined elements in the business environment. In this paper we propose the integration of systems modelling with scenario planning to support the exploration of uncertainty, identify knowledge gaps that set a (subsequent) research agenda, understand the role and usefulness of historical data, and model research outcomes to help reveal potential predetermined elements. Specifically, scenario planning identifies potential predetermined elements in the business environment; system modelling in the form of behaviour-over-time graphs, causal mapping and feedback loops helps in structuring and linking variables and their interaction to provide an understanding of the systemic drivers of these predetermined elements. Methodological integration of this type enhances modellers’ ability to support strategy in organisations.     

Infinite horizon optimal policy for an inventory system with two types of product sharing common hardware platforms

We consider a periodic review inventory system and present its optimal policy in the infinite horizon setting. The optimal inventory policy that maximizes the infinite horizon expected discounted profit for the model is analytically obtained by relating to the finite horizon setting using results from variational analysis. Results are provided that elucidate the operations of the inventory system in the long run.     

Bayesian policy support for adaptive strategies using computer models for complex physical systems

In this paper, we discuss combining expert knowledge and computer simulators in order to provide decision support for policy makers managing complex physical systems. We allow future states of the complex system to be viewed after initial policy is made, and for those states to influence revision of policy. The potential for future observations and intervention impacts heavily on optimal policy for today and this is handled within our approach. We show how deriving policy dependent system uncertainty using computer models leads to an intractable backwards induction problem for the resulting decision tree. We introduce an algorithm for emulating an upper bound on our expected loss surface for all possible policies and discuss how this might be used in policy support. To illustrate our methodology, we look at choosing an optimal CO₂ abatement strategy, combining an intermediate complexity climate model and an economic utility model with climate data.     

Modelling of linguistic variables in multicriteria energy policy support

The climate change and the increasing complexity of the energy sector along with the prerequisite for sustainability have broadened the energy policy shaping field by bringing out new challenges. Decision support tools and methods, such as Multicriteria Decision Aid (MCDA), are necessary for energy policy, in the pursuit of appropriate approaches necessary to support the restructuring of the energy sector, concerning patterns of energy extraction, generation, transformation and use, from unsustainable to sustainable forms of development. Papers devoted to the investigation of MCDA models using linguistic variables for energy policy support seem to be not available in the international literature. The scope of this paper is to explore different linguistic representation and computational models in MCDA that are or can be applied to energy policy support and to establish a clear linkage between them. This paper argues that MCDA methodologies with direct computation on linguistic variables can support energy policy frameworks, bridging the gap between energy policy makers thinking, reasoning, representation and computing. Finally, current trends, open questions and prospects in this topic are pointed out.     

A manufacturer's optimal quantity discount strategy and return policy through game-theoretic approach

This study generalised the traditional quantity discount problem with return contracts, in which a manufacturer promises to refund some fraction of the retailer's wholesale price if an item is returned, as a two-stage game. In the first stage the manufacturer and retailer determine the inventory level cooperatively. In the second stage, the manufacturer bargains with the retailer for quantity discount and return schemes to maintain channel efficiency. A menu of discount–return combinations is proposed for the manufacturer to make inventory decisions. The model developed will demonstrate that the return policy can be considered as mirror images of quantity discount strategy. That is, options with more generous return privileges are coupled with higher wholesale prices, whereas the lowest wholesale price comes with very strict limits on returns and a restocking fee for any returned goods.     

Feed-forward modelling and fuzzy logic based control strategy for powertrain efficiency improvement in a parallel hybrid electric vehicle

With the stricter limitations on both fuel consumption and air pollution, the advantages of a hybrid electric vehicle are becoming more evident than ever. In the present study, an energy management system for a hybrid electric vehicle is developed. Because the plant under consideration is nonlinear, multi-domain, time-varying, has multiple uncertainties and, in addition, the designed control strategy must be able to obey the driver's commands and achieve the par-internship for a new generation of vehicle regulations, the fuzzy logic approach is chosen. A feed-forward hybrid vehicle simulation model is used to demonstrate the validity and the convenience of the current approach and its results have been compared with the other parallel hybrid electric vehicle control strategies. Simulation results show considerable improvement in the efficiency of the internal combustion engine and, consequently, fuel consumption and acceleration performances.     

Optimal strategy policy in batch arrival queue with server breakdowns and multiple vacations

This paper considers a like-queue production system in which server vacations and breakdowns are possible. The decision-maker can turn a single server on at any arrival epoch or off at any service completion. We model the system by an M^[x]/M/1 queueing system with N policy. The server can be turned off and takes a vacation with exponential random length whenever the system is empty. If the number of units waiting in the system at any vacation completion is less than N, the server will take another vacation. If the server returns from a vacation and finds at least N units in the system, he immediately starts to serve the waiting units. It is assumed that the server breaks down according to a Poisson process and the repair time has an exponential distribution. We derive the distribution of the system size through the probability generating function. We further study the steady-state behavior of the system size distribution at random (stationary) point of time as well as the queue size distribution at departure point of time. Other system characteristics are obtained by means of the grand process and the renewal process. Finally, the expected cost per unit time is considered to determine the optimal operating policy at a minimum cost. The sensitivity analysis is also presented through numerical experiments.