Quantitatively Estimating The Cost Of Converting Now Or Later
August 14, 2008
By Charles Cresson Wood
Once management at business firms, government agencies and non-profit organizations acknowledges that there is a substantial body of evidence to support the “peak oil theory,” as some people refer to it, they will soon thereafter be asking: “When should our organization transition away from petroleum?” In other words, given the assumption that an organization will at some future point in time be forced to transition to alternative sources of energy, at what future point in time would it be most prudent to make this conversion? This question can be answered quantitatively, with a formal decision making model using decision trees and/or influence diagrams.
Generally the most difficult part of this process is convincing management that such a model should in fact be developed. But if you look for it, the quantitative evidence supporting the “peak oil theory” is readily available. For example, the Energy Information Administration web site shows that worldwide production of oil has been effectively flat at about 74 million barrels per day since 2005. Making reference to the actual historical total world petroleum production data by year, actual number of discoveries of large oil fields by year, and similar indisputable quantitative data is the recommended strategy. Discussion of how it is likely that the total world oil production curve is expected to show up something like the historical United States total oil production curve is also helpful. Stick with the facts, and avoid estimates and projections. This means avoidance of estimated remaining petroleum reserves, number of years of estimated supplies remaining, etc.
The development of a model to provide guidance about the timing of a transition away from petroleum can be approached as a capital budgeting decision. Since such a project involves a considerable amount of time and money, it deserves a detailed analysis. To simplify the model development process, we can focus on the date when substantially all of an organization’s critical business processes are converted away from petroleum. This approach assumes the organization already knows which critical business processes are dependent on petroleum-based fuels such as gasoline and petro-diesel (if it does not, an inventory of these dependent business processes is advisable).
For example, five specific conversion projects could be proposed to top management. They could involve a completed transition within (1) one year, (2) two years, (3) five years, (4) ten years, and (5) twenty-five years. The net present value, internal rate of return, payback or some other financial measure of expected value can be calculated for each of these five projects. The project with the greatest expected value would then indicate the timeframe when it would be best to have transitioned away from petroleum. To get to this single numerical ranking of the alternative projects, the analyst developing a model will need to gather additional information. While a listing of the additional information needed in order to construct such a model is clearly beyond the scope of this brief article, a number of data points that will be important to the model are provided below.
Such a model’s structure could involve two major segments. The first segment could be a traditional managerial accounting delineation of the costs of a conversion project. The second could involve a decision making model, including the expected dollar value outcomes and expected probabilities.
In this first major segment, the analyst could note that the costs to convert will be rapidly rising over the years ahead. This could reflect an expectation that commodities such as steel and lead will become considerably more expensive in the years ahead, and also that the energy required to manufacture new alternative energy equipment will most likely be more expensive in the years ahead. Likewise, these higher costs to convert in the years ahead could reflect the expectation that competition for resources and expertise in the alternative energy field will be considerably more intense in the years ahead.
Alternatively, in the first of these major segments, the analyst may specify that the costs to convert would go down in the years ahead. He or she may have great faith in the new technology currently being developed, for instance that which converts algae into bio-diesel fuel. Undertaking a transition away from petroleum further in the future would then allow these new lower-cost technologies to be utilized, while converting now does not allow that option. In such a model, the analyst may reason that the organization could switch directly to the prime successor(s) to petroleum, rather than going through one or more transitional technologies. For example, perhaps five years from now, an organization may be able to buy fully electric vehicles that have sufficient driving range to meet business needs. But if a transition was to be made right now, then the organization may at best be able to convert to plug-in hybrids, which would then later need to be traded in for fully-electric vehicles. A series of conversions could be incorporated into such a model, although this author recommends against it — a transition directly to a sustainable and renewable energy technology is a lot more predictable and manageable.
The second major segment of the model could include the anticipated scenarios that materially affect the organization in question. A decision tree or influence diagram can include a series of probabilities associated with a particular scenario or outcome. For example, the probability that the world has now reached peak oil production can be combined with the probability that the organization in question will suffer volatile oil supply relationships, oil shortages, oil rationing, and rapidly escalating oil-based fuel costs. The dollar value of the impacts can then be assessed, and when combined with the net result of these probabilities, the expected value of a particular segment of the decision tree or influence diagram can then be calculated. The future availability of and price of oil is a very serious matter because, in some instances, the impacts mentioned above will threaten the very survival of organizations. Some small trucking firms and some large airlines are now coming to appreciate this fact.
We now have a significant amount of quantitative data about the petroleum situation, so that we can build credible versions of mathematical decision-making models about the best timing of a transition away from this energy source. Thus management does not need to make an intuitive decision, throwing up its hands in frustration because there are too many unknowns and too many unquantifiable variables.
For example, we can now make estimates of the future price of oil. These estimates can include a variety of assumptions, such as a reversion to historical prices, or the extrapolation of the rising prices experienced during the last few years. We can factor in the future prices for oil found in the futures market, as well as the opinions of technical experts who have studied these matters at great length. For instance, Dr. Robert Hirsch, author of a very influential report* prepared for the US Department of Energy, which is informally known as the Hirsch Report, has been talking about $500/barrel oil in the next few years.
Probabilities can then be attached to each of these possible future price points for oil. The clarification of, and explicit documentation of these and related estimates, and the assumptions that going into making these estimates, is absolutely critical in the development of a useful decision-making model. This is not only because it will increase the likelihood of making the best decision, but also because it will illuminate the perspectives held by experts who have studied the facts. It is likely that top management in many organizations hasn’t yet been exposed to the latest information, and their familiarity with these reports, such as that one by Dr. Hirsch, will most likely change the way they are looking at the conversion process. This changed perspective will then most likely markedly alter the probabilities that management assigns to different possible future oil price points, as well as other numbers used in these models.
Another important quantitative assumption in such a decision-making model is the time when the availability of oil will rapidly decline. A variety of expert oil geologists are now suggesting that the total world oil supply will go down 5-10% per year for the next decade or so. Factoring in different scenarios about the availability of oil, and the adverse impacts of shortages on the organization in question, this too will be a critical part of a successful decision-making model. For example, the oil available to consumers in oil-importing countries may fall off quite rapidly if oil-producing countries choose to use increasing percentages of their oil production for their own internal populations. According to data from the Energy Information Administration, this increased domestic consumption is already happening in a number of oil-producing countries such as Saudi Arabia.
One of the great parts about preparing a model such as this is the ability to perform sensitivity analysis. Thus the model’s inputs can be altered and the results calculated again to see if a materially different result is obtained. A sensitivity analysis could thus be performed on a completed model to see at what points the result would be different. For example, if management believed that such a conversion three years from now would be most prudent, a sensitivity analysis could be conducted to determine which of the input factors must change, and by how much, before it would be clear that the most prudent course of action would be a conversion one year from now. The results of such a sensitivity analysis may surprise management, and cause them to rethink their assumptions. For example, if oil prices escalate a lot faster than expected, the target completion date for a conversion project may need to be moved forward considerably.
In summary, it is now possible for organizations of all types and sizes to calculate the best date for a conversion of all major business processes away from petroleum. When top management actually goes through this important exercise it will, most likely, become considerably more aware of the urgency of this transition.
Charles Cresson Wood, MBA, MSE, is an alternative fuels management consultant with Post-Petroleum Transportation, in Sausalito, California. His most recent book is Kicking The Gasoline & Petro-Diesel Habit: A Business Manager’s Blueprint For Action. To learn more about the book, to read his alternative fuels blog, or to contact him, go to www.kickingthegasoline.com.
* This highly recommended report, which is written in non-technical language, can be found at: http://www.netl.doe.gov/publications/others/pdf/Oil_Peaking_NETL.pdf