Energy, Natural Resources and Environmental Economics

Sovereign wealth funds (SWF) are a new name for assets held by governments in another country’s currency. These funds are growing at an unprecedented rate and are becoming important players in global financial markets. In this paper, I describe how these funds are being invested and I develop a classification of investment options available for sovereign wealth funds.

Oil companies are concerned to replace the petroleum reserves they produce in order to maintain their future level of activity. Reserves also represent an important input when analysts value these companies. However, they have to rely on booked reserves, which are subject to measurement errors. Many producer countries want to control their own resources, a goal which can come into conflict with the desire of the international companies for booked reserves. Where oil companies do not own the reserves, they may have insufficient incentives to maximise value – harmonising goals between a resource country and an oil company can be difficult. This chapter discusses the relationship between reserves and financial incentives, and between reserves and valuation. The issues are illustrated throughout with reference to two cases: StatoilHydro’s projects on Shtokman in Russia and Peregrino in Brazil.

Predictions from the original geophysical approach to oil exploration and production suggest that oil production will develop according to a predetermined and inflexible bell-shaped trajectory, quite independent of variables relating to technological development, economics, and policy. Exploring the potential sources of elasticity in oil reserves and production, this paper offers a modification to the geophysical approach. Based on economic theory and modern empirical research the results suggest that both reserve-generation and production are indeed influenced by factors and forces of technology, economics, and government regulation.

This chapter discusses various attempts to apply mathematical programming tools and techniques in field development planning for the Norwegian continental shelf. The paper has a form of a (non-complete) survey, with the aim of discussing and presenting various attempts, both within deterministic and stochastic modelling.

In this paper, we provide an overview of the natural gas value chain, modeling aspects and special properties that provide challenges when doing economic analysis. We present a simple value chain optimization model and discuss important properties of this model.

Several features may separately or in combination influence conduct and performance of an industry, e.g., the numbers of sellers or buyers, the degree of economies of scale in production and distribution, the temporal and spatial dimensions, uncertainty about long run development of demand in particular combined with large investments in production capacity and infrastructure, etc. Our focus is modeling in order to obtain insight into market mechanisms and price formation. In particular, we demonstrate the rather different solutions obtained from the price-taking behavior versus the oligopolistic Cournot behavior when the spatial dimension is observed.

The purpose of this paper is to enhance the insights of whether and, if so, how equilibrium models can enhance managerial team learning in complex and ever-changing situations. To handle this research challenge we first clarify the concept of managerial team learning. Then, we present an example of an equilibrium model, VisualGas, which has been developed to help managers in a large oil and gas company to improve their understanding of market behavior. We report on a quasi-experimental study where a team of managers/market analysts evaluated the consequences for the company of a critical market event, first without and then with the system. It was found that use of the system led to a more varied interpretation of the event, i.e., learning, but also to a focus on the system variables only.

In this chapter, we give an overview of recent literature on the planning and scheduling of refinery activities. Planning of refinery activities ranges from determining which crude oil types to acquire to which products that should be produced and sold in the market. The scheduling ranges from scheduling of crude oil unloading and blending to blending of components to finished products. This overview treats three different categories of activities: planning and scheduling of crude oil unloading and blending, production planning and process scheduling, and product blending and recipe optimization. The focus will be on identifications of problems, the models outlined for the specified problems, and the computational difficulties introduced by the models. A final section discusses an agenda for future research.

In this paper, we exploit multivariate and functional data techniques to capture important features concerning the time dynamics of hourly one-day ahead electricity prices at Nordpool. The methods are also used to obtain pragmatic prediction schemes for these prices. Following Huisman et al. (Energy Economics 29, 2007), we first consider the 24-hourly one-day ahead prices as a 24-dimensional variable observed on a daily basis. These are analyzed by means of a multivariate analysis of variance (MANOVA) and the results are presented by some enlightening graphs. We then account for the smoothness of the curve that these 24 values form when they are seen as a function of time of the day. This smoothness is exploited by a functional analysis of variance (FANOVA). Multivariate prediction is then addressed, first by a simple univariate ARIMA scheme combined with hourly corrections obtained from the MANOVA and then by a true multivariate state space scheme.

Electricity is a nonstorable commodity. Consequently, electricity prices will follow fairly regular fluctuations in demand, stemming from time dependent variations in economic activity and weather conditions. However, it is possible to store electricity as a different energy carrier (e.g., hot water) and both consumers and producers have some leeway for changing behavior in order to take advantage of price regularities. Thus, the price regularities should be within arbitrage limits, and one would expect price regularities to be reduced over time as a result of investments that increase flexibility in consumption as well as production. In this article, hourly, daily, and weekly prices and price changes at the Nordic power exchange (Nord Pool) are analyzed over the period January 1995–December 2006. The tentative conclusion from the statistical analysis is that the price regularities may offer potentials for profitable investments in flexibility as well as profitable trading strategies.

The purpose of this paper is twofold: Firstly, we analyse the option value approximation of traded options in the presence of a volatility term structure. The options are identified as: (a) “European” (written on the forward price of a future flow delivery); and (b) “Asian”. Both types are in fact written on (arithmetic) price averages. Secondly, adopting a 3-factor model for market risk which is compatible with the valuation results, we discuss risk management in the electricity market within the Value at Risk concept. The analysis is illustrated by numerical cases from the Norwegian electricity derivatives market.

We provide an overview of stochastic programming models in short-term power generation scheduling and bidding. Special emphasis is placed on the development prompted by the restructuring of the electricity sector.

The objective of this work is to present a decision support system that determines the optimal dispatch strategy of thermal power plants while considering the particular specifications of fuel supply agreements, such as take-or-pay and make-up clauses. Opportunities for energy purchase and selling at the spot market as well as a detailed modeling of the power plant (maintenance cycles, influence of temperature, etc.) are also considered during the optimization. In an integrated approach, the model also determines the plants’ optimal schedule for maintenance. Since decisions in a stage have an impact in the future stages, the problem is time-coupled with a multi-stage framework. Moreover, the main driver for the decision-making is the energy spot price, which is unknown in the future and is modeled in this tool through user-defined scenarios. Therefore, the calculation of the optimal dispatch strategy is modeled as a decision under uncertainty problem, where at each stage the objective is to determine the optimal operation strategy that maximizes the total revenues taking into account the constraints and characteristics of the fuel supply contract. The methodology applied is a hybrid Stochastic Dual Dynamic Programming (SDDP)/Stochastic Dynamic Programming (SDP). Examples and case studies will be analyzed for the Brazilian system.

We discuss a portfolio optimization problem occurring in the energy market. Energy distributing public services have to decide how much of the requested energy demand has to be produced in their own power plant, and which complementary amount has to be bought from the spot market and from load following contracts. This problem is formulated as a mixed-integer linear programming problem and implemented in GAMS. The formulation is applied to real data of a German electricity distributor.

This study investigates the advantages of investing in plants for cogeneration, i.e., Combined Heat and Power (CHP), in case the heat is utilized for district heating. A focus is set on Swedish municipalities. The demand for heat is visualized in terms of load curves and duration diagrams. A standard diagram is chosen in order to analyze the dimensioning of a CHP plant. Two main alternative dimensions are analyzed in depth, namely to operate a plant with full capacity during eight months or alternatively during six months of the year. For each alternative, a CHP plant is compared to a heat water plant (a “boiler”) and a biological fuel is compared to the one of natural gas. Then, further expansions are analyzed in a parametric way. The outcome is that it is efficient to choose the dimension so large that it will only be operating at full scale during three months of the year. It is also shown that CHP plant based on biological fuel is profitable and outstanding. These theoretical findings are then illustrated by data taken from 10 large Swedish municipalities – Göteborg, Helsingborg, Linköping, Lund, Malmö, Norrköping, Stockholm, Uppsala, Västerås, and Örebro. However, even if cogeneration is an energy efficient way to supply electricity and heat in these municipalities, there are constraints to invest. Examples are contracted deliveries of heat from outside, existing old plants, average cost pricing, and uncertainties in terms of future taxation principles.

In this paper, we suggest a procedure based on capacity charges for managing transmission constraints in electricity networks. The system operator states nodal capacity charges for transmission prior to market clearing. Market clearing brings forth a single market price for electricity. For optimal capacity charges the market equilibrium coincides with that of optimal nodal pricing. Capacity charges are based on technical distribution factors and estimates of the shadow prices of network constraints. Estimates can be based on market information from similar congestion situations, and then capacity charges can be brought near the optimal values through an iterative process.

Regulators for electricity network infrastructure, such as electricity distribution system operations (DSOs) face some particular challenges in the Nordic countries. Due to institutional, economic, and historical reasons the DSOs in the Nordic area are relatively numerous and heterogeneous in terms of ownership structure, size, and operating conditions. Since the deregulation in 1994–1999, the national regulators have independently devised regulation mechanisms that address the heterogeneity through econometric or engineering cost models as a basis for high-powered regimes. The frontier analysis models (such as data envelopment analysis in, e.g., Norway and Finland) are particularly useful here, given their incentive properties and cautious estimation of the production set. However, the total information rents in yardstick regimes and the bias in the frontier estimation are related to the number of observations (firms), which undermine their future application in the Nordic area under increasing interregional concentration. This paper develops a proposal for an alternative model, the revenue yardstick model, that can be applied across the national regulations and permit frontier estimations on final user cost rather than cost estimates, sensitive to, e.g., capital cost estimates, periodization, and allocation keys. The core of the model is a dynamic frontier yardstick model such as Agrell et al. (2005), but here applied only to strictly exogenous conditions, the output dimensions and the claimed revenues of the DSO. An equilibrium is implemented using asymmetric penalties for positive and negative deviations from the ex post frontier revenue, the yardstick, using the classic superefficiency model in analogy with Shleifer (1985). The model is particularly aimed at an international (interregional) application as it may embed national differences in regulation without jeopardizing the long-term sustainability of the model.

In this paper, we give an overview of the Norwegian regulation of electricity networks after the Energy Act of 1990 and the deregulation of the electricity markets in 1991. We concentrate on the regulatory oversight of distribution network companies and regional transmission. Our main focus is on the benchmarking models, including the application of their results, in the three periods of incentive regulation that we have seen so far, after its introduction in 1997. We examine the various data envelopment analysis (DEA) models that have been used, and we describe specific issues driving their development and how the results have been used.

This chapter discusses elementary properties of allowed depreciation and return on the asset base for a regulated company under two regulatory models, the traditional rate-of-return model and the more recent LRIC model. Under the former model, any method of computing depreciation and return on the asset base is, in principle, fair to the regulated company and its customers. Under the latter model, only the real annuity method is fair.

A predator–prey model is used to analyse the case where the prey has been overexploited for a while and therefore is threatened by extinction even along the optimal harvesting path due to depensation in the biological model. It is assumed here, however, that extinction is unacceptable for non-economic reasons. Various sub-optimal rescue operations involving increased harvest of the predator and reduced, or zero, harvest of the prey are therefore considered. The question is how and when it is possible to rescue the prey from extinction by departing from the optimal path. Such sub-optimal policies are not always feasible. If they are feasible, they imply certainly reduced profits and may even produce negative profit. The objective of this chapter is to find the criteria for when a rescue operation is feasible and to explore the dynamics of this situation.

Norwegian paper and pulp mills are subject to strict environmental regulation. The mills conduct research and development for reducing pollution. Absorptive capacity indicates their competence. Firms are part of a social network of connections with external actors that include other paper and pulp mills, suppliers, customers, research institutes, and universities that help them in developing technologies. These relations represent their social capital. Some firms have access to more and better resources than other firms. Measuring firms’ absorptive capacity and access to social capital, we analyze their success in reducing pollution levels. There is a strong interaction effect between absorptive capacity and social capital. The effectiveness of social capital depends on absorptive capacity, and absorptive capacity is not effective without social capital.

Collaborative logistics is becoming more important in today’s industry. This is driven by improved economic and environmental efficiency through collaborative planning supporting resources sharing and new business models implementation. This paper presents a survey of contributions to the field of collaborative logistics. It first describes current opportunities in collaborative planning. It then discusses important issues related to building the coalition, sharing resources and benefits, as well as related to information and decisions technologies. Business cases are described and used to support the discussion. Finally, questions are raised, opening new paths for researchers in the field.

The Maritime Administration in Sweden initiated a study on the best allocation of pilots and its impact on costs and service levels. Pilots are required to guide ships from and to ports along the shipping fairways between the ports and open sea. Transports to and from rendezvous points at sea are made by small vessels. Normally the pilots are qualified to work on a subset of the shipping fairways. Average workloads are known from historic data, but the actual times for the required services are more or less known some 20 + h in advance. The problem studied was a historic data set covering 1 week of operations involving 66 pilot assignments and 21 pilots. A column generation approach was adapted. It includes the solving of shortest path problems in time–space graphs. Postprocessing is applied to handle legality constraints, which involve maximum work time shifts and minimum rest times in between. Set partitioning and set covering models were deployed to identify the final solution. Any multiple assignments associated with set covering formulations were handled by a myopic cost estimation procedure, and a small integer program for surplus removals.

In this chapter, the LNG supply chain is introduced, and two planning problems related to the transportation planning and inventory management within the chain are presented. The two problems have different characteristics and reflect the planning situations for a producer and a vertically integrated company, respectively. Both problems are formulated as mixed integer programs, and possible solution methods are briefly discussed.

We present a combinatorial optimization model for the relinquishment of petroleum licenses in accordance with the rules given in the legislation concerning the Norwegian continental shelf. It is shown that a relaxation of the model can be interpreted as the problem of finding a minimal connected component of given cardinality in an undirected grid graph with node weights. We discuss the differences between the rules in 1973 and 1991, and how they lead to different mathematical models. A discussion of the complexity of the mathematical problem for optimal relinquishment is presented, and a presentation of related graph theoretical models is given. We conclude the paper with a small illustrative example, which shows the effects of the different restrictions. References to other studies in which large scale examples are treated are given.

Network flow models, where the flow quality is given at the supply nodes and quality constraints are defined at the demand nodes, appear frequently when optimizing refinery operations, pipeline gas transportation, and other energy related operations. Tracing the quality of the flow from the supplier to the market implies that the quality must be updated at the nodes where different flow streams are mixed. This results in the well-known pooling problem, the non-linear nature of which makes it hard to solve. In this chapter, I give an overview of available methodology for solving the pooling problem. I distinguish between fast but possibly inexact methods aimed for medium and large scale instances, and more time-consuming exact methods that tackle small instances.

Exchange of contingent claims is construed here as a cooperative game with transferable utility. Solutions are sought in the core. The novelty is that agents, being uncertainty averse, may use distorted, subjective probabilities. Choquet integrals therefore replace expected utility. When convoluted payoff is concave at the aggregate endowment, there is a price-generated, explicit core solution.

We solve a specific optimal stopping problem with an infinite time horizon, when the state variable follows a jump-diffusion. The novelty of the paper is related to the inclusion of a jump component in this stochastic process. Under certain conditions, our solution can be interpreted as the price of an American perpetual put option. We characterize the continuation region when the underlying asset follows this type of stochastic process. Our basic solution is exact only when jump sizes cannot be negative. A methodology to deal with negative jump sizes is also demonstrated.

The development of computer technology has made discrete event simulation (DES) an increasingly attractive method. This chapter starts with a brief survey of the important uses of DES within the Energy, Natural Resource and Environmental Economics area. The chapter then describes three examples of how the work relating to this area using DES has done, namely: (1) models of project management, where simulation models allow for more realistic assumptions of time distributions and of limited resources than standard PERT (Program Evaluation Review Technique) and CPM (Critical Path Method) methods; (2) a bidding situation for oil resources, characterized by asymmetric information; (3) a small game dealing with duopolies producing and selling homogenous goods, such as oil or coal, but where demand is stochastic.