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Long term oil prices
Energy Economics 58 (2016) 84–94
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Energy Economics journal homepage: www.elsevier.com/locate/eneco
Long term oil prices Erik Haugom a, * , Ørjan Mydland a , Alois Pichler b a b
Lillehammer University College, Lillehammer, Norway The Norwegian University of Science and Technology, Trondheim, Norway
A R T I C L E
I N F O
Article history: Received 19 August 2015 Received in revised form 30 March 2016 Accepted 29 June 2016 Available online 12 July 2016 JEL Classification: A12 C00 E27 E37
A B S T R A C T In this paper we propose a model to estimate and simulate long term oil prices. Our model is based on properties of demand and supply for oil and it is able to reproduce historical real oil prices well. We use the model to estimate and simulate future real oil price scenarios. The results show that if we are not able to significantly increase demand elasticity, the yearly real oil price change can reach 12% in the years following the peak production level without taking a scarcity rent into account. Until peak production level is reached, the long term real oil price changes stemming from fundamental supply and demand changes are expected to be negative. Our simulation results based on an expected peak production year of 2020 and a scarcity rent of 3% suggest an expected real crude oil price of $169/bbl in 2040. For comparison the EIA outlook predicts a real oil price of $141/bbl for the same year. We also provide an on-line Appendix that allows the readers to change the assumptions underlying our analysis and see the results immediately. © 2016 Elsevier B.V. All rights reserved.
Keywords: Oil prices Energy dependence Oil supply Price elasticity of demand and supply
1. Introduction Oil price forecasting is known to be a challenging task. The large number of variables affecting the price, the non-linear effects and feedback loops, and all the “unknowns” and uncertainties can quickly compound into very different estimates depending on who you ask. Maybe for this reason the outlooks that are presented and published have been dominantly concerned with the price development for a few years ahead. Only occasionally researchers aim to model oil and other energy prices into the more distant future. Given the importance of crude oil as the world’s primary energy source in general it is crucial to have a good understanding of how these prices behave in the long term, without getting blinded by all the details and noise influencing the short term prices. In this paper we propose a simple model for the long term oil prices that builds on characteristics of demand and supply. The key element in our model is the fundamental relationship between price, quantity and price elasticity. We compare the estimates from our
* Corresponding author. E-mail addresses: [email protected] (E. Haugom), [email protected] (Ø. Mydland), [email protected] (A. Pichler).
http://dx.doi.org/10.1016/j.eneco.2016.06.014 0140-9883/© 2016 Elsevier B.V. All rights reserved.
model with historical data of real oil prices and show that it is able to reproduce the long term price patterns. Long term price movements are quantified and illustrated through a simulation study. The main result based on our model is that the yearly real oil price changes can reach approximately 12% in the years following the peak production level, without taking the scarcity rent into account.1 These estimates are based on the assumption that price elasticity of demand will stay within the current, “inelastic”, estimates reported in the literature and without considering structural shifts in the demand curve itself.2 However, our in-sample results show that the fundamental relationship between supply and demand is only partly able to explain the long term development of the real oil price. That is, when we “control for” a scarcity rent that increases with the rate of interest, the in-sample fit is improved substantially particularly for the last part of the in-sample period. This indicates that the scarcity rent is becoming more and more important in explaining
1 It is important to note that we talk about the long term price changes in the oil price. That is, yearly price changes can be substantially less and above these estimates due to short term conditions occurring on irregular basis. We will elaborate more on this in the subsequent sections. 2 Price elasticity of demand can primarily be increased by investing in alternative energy sources and preferably through renewable energy sources.
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the long term oil price movements. The results from the out-ofsample simulations suggest that the model can be used to obtain reasonable estimates for future real oil prices. For example, given an expected value of five years until peak production and a scarcity rent of 3% implies an expected oil price of $169/bbl in 2040.3 The official forecast from EIA for the same year is $141/bbl. 1.1. This paper is organized as follows Section 2 provides some information about the properties of crude oil and how the oil market has developed over time. Section 3 reviews some of the literature within oil price modeling and forecasting. In Section 4 we present the theoretical economic relationships for supply and demand and propose the model that can be used to understand long term price developments. In Section 5 we show the results of our model and compare them with actual historical data. We also present future oil price scenarios based on the model. Section 6 concludes and provides implications for policy makers and others working within the area of energy security. 2. Crude oil 2.1. Some properties of crude oil Today crude oil is heavily traded all over the world and it is the most important energy commodity both in physical and monetary terms. Crude oil is not a homogeneous product though. How effectively oil can be processed into refined products depends on a number of characteristics such as (i) specific gravity (density), (ii) sulfur content, (iii) total acid number (TAN), (iv) viscosity, and (v) the percentage of vacuum residue (VR).4 A simple, yet useful measure of the energy obtained from various sources is the term energy return on energy investment (EROI), which can be defined as EROI =
Energyoutput . Energyinput
(1)
In Eq. (1), Energyinput is related to all direct and indirect energy used to produce the final product. For example, indirect energy input can be related to production of capital equipment needed for producing a given form of energy (Kaminski, 2012).5 The world’s total oil reserves consist of conventional and unconventional oil. To simplify we can say that whether oil is conventional or unconventional is determined by the cost and complexity in extraction, and thus related to the EROI. Conventional oil has a relatively high EROI, while unconventional oil, such as tar sands oil, has a low EROI. As Greene et al. (2006) point out, the petroleum resources falling into the two categories are likely to evolve over time as technology improves. Fifty years ago, offshore crude oil was considered unconventional, but is today regarded as a conventional resource (Adelman, 2003). The International Energy Agency (IEA) defines the difference between conventional and unconventional oil as follows: “Conventional oil is a category of oil that includes crude oil and natural gas liquids and condensate liquids, which are extracted from natural gas production. Crude oil production in 2011 stood at approximately 70 million barrels per day. Unconventional oil consists of a wider variety of liquid sources including oil sands, extra heavy oil, gas to liquids and other liquids. In general, conventional oil is easier and cheaper to produce than
3 This estimate is based on various values for the other input variables defined in Table 1. 4 See Kaminski (2012) for a description of these characteristics and other properties influencing the overall quality of crude oil and refined products. 5 For a thorough review of the concept of EROI we refer to Murphy and Hall (2010).
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unconventional oil. However, the categories “conventional” and “unconventional” do not remain fixed, and over time, as economic and technological conditions evolve, resources hitherto considered unconventional can migrate into the conventional category.” (IEA, 2014) Whether oil is classified as unconventional or conventional does not affect the geological limits of how much oil is left to be extracted, only how “easy” or “hard” it is to extract the oil that is in the ground using the technology at hand.6 2.2. Oil reserves and production Accurate measures of the oil reserves are important in order to predict future supply and prices. However, different definitions, errors, omissions, double counting, and the dynamic aspect of exploration, make it a very challenging task to obtain good estimates of the total oil reserves at any given point in time. Given the facts about the geological process of “creating” new oil and fossil fuels in general, we can assume with complete assurance, as Hubbert (1956) did, that the total world supply of oil, as we know it, is initially fixed “to which there will be no significant additions during the period of our interest” (Hubbert, 1956, p. 4). This view is not necessarily the same as saying that world oil production is peaking in the near future, only that the total “stock” of oil is fixed due to the extreme lead time. However, in the academic discussion about oil resources and production, there are scholars arguing that limitations of oil resources are irrelevant due to technological change and market forces (see e.g., Adelman, 2004). In this discussion over oil resources and production we can broadly divide the participants into two groups: the “pessimists” and the “optimists”. The former group is interested in peaking curves using similar methods to those of Hubbert (1956) and generally believes that geology will be more important than economics or technology in determining when oil production will peak. The participants of the latter group (the optimists) are often referred to as “economists” because of their strong belief in the market mechanisms and technological innovation (Greene et al., 2006). Our approach falls in between these two viewpoints. We assume that the total worldwide oil reserves are fixed and that the long term supply can be approximated well by using the approach of e.g., Hubbert (1956).7 Then we use the fundamental relationships between supply and demand to evaluate possible future outcomes for the real oil prices. The challenges with estimating the total global oil reserves are not only related to when peak production will occur, but also the availability of historical data. Fig. 1 illustrates historical oil production from two different sources. These numbers date back to 1965 (British Petroleum, BP) and 1980 (Energy Information Agency, EIA), respectively.8 Though these historical numbers from the two sources are fairly similar, the EIA estimates are somewhat larger than the BP estimates. The difference in the 2013 daily production numbers is approximately 3.5 million bbl/d.9 In the long run, such differences, and the eventual short-term ups and downs in the
6 What is currently considered to be the cutting edge technology can be the standard technology in five or ten years, and so forth. However, as new technology also can be very energy intensive a decline in future EROI is still likely. We are thankful to an anonymous referee for pointing this out. 7 Though we use various functions for estimating the total supply of oil, we assume that all the production curves exhibit a common property of beginning at zero and ending at zero after passing through a maxima. We will briefly consider movements in supply related to the OPEC cartel though in the next sub-section. It is important to note that such movements are of relative short term in our framework. 8 Despite an extensive effort, we have not been able to find historical oil production data for a longer time horizon than this. 9 Some of the differences in the production numbers are definitional. We are indebted to an anonymous referee for pointing this out and refer interested readers to Jean Laherrere’s ASPO France webpage (http://aspofrance.org/) for definitions.
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Fig. 1. Historical world oil production from BP (solid line: 1965–2013) and EIA (dashed lined: 1980–2013).
production level, are of minor importance for the purpose of this paper.10 We will return to how we estimate historical and future production levels in Section 4. 2.3. Historical prices Fig. 2 illustrates the development of real oil prices from 1861 to 2013. It is evident from this figure that real oil prices have undergone many small and large changes over time. In the very beginning of the market place for oil, prices were extremely erratic and reached almost 120 $/bbl in real terms (2013 dollars) in 1864. The high volatility of the oil market kept on until the early 1870s. Such a high volatility in the early days of the market is somewhat expected as the uncertainty about supply and demand was high. For the approximately 100 years to follow (1874–1974) the yearly real oil price changes were relatively modest. However, from 1961, when OPEC was established, both the oil price, and the volatility of the oil price, became higher compared with the previous period from around 1880 to 1960.11 It is important to note, however, that we are not particularly interested in understanding the short term shocks or fluctuations in this paper, but rather the long term developments. Therefore, we have superimposed a 25 years moving average (dashed line) and a quadratic trend line (dotted line) in Fig. 2. It is evident from the figure that the long term prices generally fell until the 1940s/1950s, a period during which the production of oil increased rapidly (Pindyck, 1999). From that point onwards, the real prices have generally increased and the quadratic trend line seems to provide a good approximation as pointed out by Pindyck (1999). However, such a trend line does not take into account any information about fundamental aspects of supply and demand, it only uses the properties of historical prices. In the next section we briefly review various approaches researchers have used to understand long term oil prices.
either reserves, future supply or (peak) production (see e.g., Greene et al., 2006; Kjärstad and Johnsson, 2009; Kerr, 2011; Maggio and Cacciola, 2009), taxation (Dasgupta et al., 1981), oil price shocks based on properties of supply and demand (see Kilian, 2008; Kilian, 2009), or (relatively) short term forecasting of oil prices and consumption (see e.g., Gori et al., 2007, and the references therein). One reason for why so little attention has been given to long term oil price modeling and forecasting may be that it is known to be difficult due to ambiguous or poor information about the true global oil resources (Kjärstad and Johnsson, 2009), and the complexity of both the characteristics of the commodity and the market mechanisms in general. One recent study that focuses on understanding the behavior of oil prices is the seminal paper of Hamilton (2009). In this paper Hamilton (2009) explores three broad ways to explain oil price changes: (i) statistical investigation of historical data, (ii) predictions of economic theory, and (iii) fundamental determinants and prospects for demand and supply. The overall conclusion of that paper is that: “. . . the low price-elasticity of short-run demand and supply, the vulnerability of supplies to disruptions, and the peak in U.S. oil production account for the broad behavior of oil prices over 1970–1997. Although the traditional economic theory of exhaustible resources does not fit in an obvious way into this historical account, the profound change in demand coming from the newly industrialized countries and recognition of the finiteness of
3. Modeling and forecasting long term oil prices Despite our great oil dependence, there is remarkably little research aiming at understanding the long term real oil price development. The focus of the research within the oil sector is often on
10 For example, the Organization of Petroleum Exporting Countries (OPEC) was established in 1961 by Saudi Arabia, Iran, Iraq, Kuwait and Venezuela to coordinate oil policy in the member states, and to stabilize price fluctuations. To keep the price at a high level, OPEC has limit their total production and each member country has a production quota. This however, could provide an incentive for the member countries to report higher reserve estimates. 11 For a review of the history of OPEC we refer interested readers to Dahl (2004) and Bernanke et al. (1997).
Fig. 2. Development of real oil prices from 1861–2013 (solid line) and corresponding 25 years moving average (dashed line). The dotted line is a fitted quadratic trend line as done in Pindyck (1999). Base year for the real oil price calculations is 2013. Source: Source: Petroleum (2014).
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this resource offers a plausible explanation for more recent developments. In other words, the scarcity rent may have been negligible for previous generations but may now be becoming relevant. . . ” (Hamilton, 2009, p. 180). Though the paper uses relatively long period of sample data,12 it does not include prices from the early days of the oil market, as we know it. This however is done in Pindyck (1999) who examines real crude oil prices over a 127-year period (1870–1996) using a statistical approach.13 In this study Pindyck (1999) argues that real energy prices, including crude oil prices, is best described by a multivariate stochastic process. That is, the behavior of such prices suggests reversion to trend lines with slopes and levels that are both shifting continuously and unpredictably over time. The approach used in this paper does not include prospects of supply and demand but rely on correlations in historical data.14 One of few recent attempts of modeling long term oil prices and linking this with fundamental information about oil supply is the study by Rehrl and Friedrich (2006). In this paper, they use the framework of Hubbert (1956), but embed profitability and technical progress. Their main finding about the long term oil price is that “. . . a significant higher oil price beyond 2010 is expected in any LOPEX15 scenario, compared to the reference scenario that is currently used for policy decisions . . . Very high oil prices certainly would go along with heavy structural changes on the demand side which is probably not covered in LOPEX”. In our model, we particularly focus on the demand side, and show the long term real oil price scenarios for various properties of supply and demand in particular. 4. Theoretical relationships 4.1. Supply and demand As done in Hubbert (1956) we assume that oil is a resource with an initially fixed supply in the earth to which there will be no significant additions during the period of interest. Hence, at some point in time the production will reach a peak and then eventually start to fall. Such a continuous drop in production may be viewed as negative shifts in the supply. The slope of the demand curves determines the effect on the price from a drop in oil production.16 The steeper the slope of the demand curve the bigger the price increase from a decrease in the supply. The relationship between the steepness of the demand curve and the effect on the price is described well in the seminal paper of (Hamilton, 2009, p. 186). He writes: “. . . suppose we take it as given that as a result of unavoidable geolocial limits, global production of crude oil next year
12 The sample of West Texas Intermediate average monthly prices is for 1947:M1 through 2008:M10. 13 In this paper the long term price characteristics of bituminous coal and natural gas are also examined. 14 The time-varying aspect is accounted for using a Kalman filter. 15 LOPEX is the name of their model. 16 The slope of the supply curve will also have an impact on the price. Previous research reports very inelastic supply of oil (Krichene, 2002). This essentially means that oil producers will extract the available oil without doing too many considerations about the price. This supports the use of production curves for the supply which follows the general pattern of those presented by Hubbert (1956) and others. We recommend the seminal paper of Hallock et al. (2014) and the various references therein when it comes to modeling and forecasting oil production and demand. It is also important to note that the properties of any production curve can be adjusted easily, and it is not the objective of this paper to find the best possible production curve for crude oil, but rather illustrate how our model can be used for various production curves and prospects for demand characteristics.
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could only be 90% of the amount being produced this year. If we assumed say a short-run demand price elasticity of −0.10, that would imply a price of oil next year that is twice its current value.” Fig. 3 illustrates this principle by depicting price as a function of price elasticity of demand and the magnitude of the supply change. Using the example of Hamilton (2009), where global oil production drops 10% next year and the short-term demand price elasticity is −0.10, the figure shows that the price of oil next year will be twice its current value. For a more elastic demand curve (e.g., −0.50) the impact on prices will be substantially less for the same reduction in supply. The principle of prices exceeding marginal production costs for exhaustible resources even if the market is perfectly competitive dates back to Harold Hotelling in 1931 (Hotelling, 1931). We will return to this important aspect in Section 4.2. Price elasticities of supply and demand are crucial inputs in our analytical model. Several studies have estimated these quantities using various approaches. Cooper (2003) estimated the long-run price elasticity of demand to be −0.21 by using an annual time series regression on averages of 23 countries, while Dahl (1993) reports a long-run price elasticity of demand estimate of −0.30 for developing countries by doing a literature survey. Krichene (2002) reports price elasticity of demand of between −0.005 (1973–1999) and −0.13 (1918–1973) using a cointegration approach. By examining previous empirical research Hamilton (2009) reports findings of the short-run elasticity of gasoline to be around −0.25 and a long-run elasticity 2 or 3 times as large. The corresponding numbers for oil are ranging from −0.05 to −0.07 in the short-run to −0.21 to −0.30 in the long-run. Indirectly, all these findings suggest that we are extremely oil-dependent. If the price of oil increases we will hardly use any less oil at all. Put differently; the slope of the demand curve is very steep, and a negative shift in the supply curve will have a big impact on the price. Corresponding supply elasticities are generally found to be larger (in absolute terms), and Krichene (2002) reports estimates of between 0.10 (1973–1999) and 1.10 (1918–1973) for this quantity. In general, he finds that both supply and demand have become more inelastic (approaching zero) over the years.17 However, as both the demand curve and supply curve are shifting due to a number of factors, it is very challenging to obtain precise estimates of elasticities in general. In the analyses we will therefore show the results using a range of numbers for the price elasticity of demand and supply.18 Fig. 4 illustrates four different paths for the price elasticity of supply and demand over time. In the upper panel the elasticities are constant across the whole period (−0.2 for the demand elasticity and 0.4 for the corresponding supply elasticity). In the lower three panels we let both elasticities change over time. The upper middle panel reflects discrete changes where price elasticity of both demand and supply becomes more inelastic over time. The lower middle panel depicts a case where both demand and supply elasticity are changing continuously, from (relatively) elastic to inelastic and back to (somewhat) elastic into the future. Such a pattern would reflect a situation where we are able to be less “oil dependent” in the future. The lower panel is identical to the lower middle panel but the starting point for the two elasticities is different. In this case the price elasticity of demand is more inelastic at all times, while the price elasticity of supply is more elastic. Hence, the difference between the two (the difference in absolute value) is increased compared to the numbers in the panel above. All the numbers for the price elasticities for
17 Krichene (2002) uses data on world crude oil output and prices from 1918–1999 to estimate supply and demand elasticities. 18 If utilizing publicly available crude oil price/production forecasts, the model can be used to estimate elasticities implied by the forecasts. This is beyond the scope of this paper, but is certainly an interesting direction for future work.
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For our purpose, the functional form of the scarcity rent over time is of minor importance. The relevant point is that the fundamental theoretical relationship between supply and demand and principles about the scarcity rent can be combined in one model framework. In the next section we present our analytical model. This model will then be evaluated in-sample before we show out-of-sample scenarios using various properties of supply and demand. 4.3. Analytical model The basic assumption is the relationship e :=
P Q
•
DQ , DP
(5)
where the quantity e is the price elasticity of demand. By reallocating this formula we can also write the relationship as DP 1 DQ = • . P e Q Fig. 3. Yearly oil price change as a function of production change and price elasticity of demand.
both supply and demand are within, or close to, the range of previous findings in the literature as mentioned above. Demand may also shift due external factors such as real income, price of related goods, population size and composition. Hence, as pointed out by Hamilton (2009), both the demand curve and the supply curve are shifting as a result of a number of factors in any given year. Even though it may be hard to come up with an exact number of how much of the quantity change from one year to another that is supply and demand driven, our model is able to take such movements, and elasticities of both supply and demand, into account. This will be elaborated in Section 4.3. 4.2. Scarcity rent The use of one barrel of oil today implies that this barrel cannot be used at any point in the future — it is gone forever. This “lost future benefit” can be measured as a cost. This cost is commonly referred to as the scarcity rent, and it dates back to Harold Hotelling in 1931.19 Formally, the scarcity rent, P t , can be defined as the difference between the price Pt and the marginal production cost Mt : Pt = Pt − Mt .
(2)
According to Hotelling’s principle, the scarcity rent should then rise at the rate of interest as shown in Hamilton (2009), such that Pt+1 − Mt+1 = (1 + i)(Pt − Mt ).
(3)
Using the relationship in Eq. (2) we can express the scarcity rent as Pt+1 = (1 + i)(Pt ).
That is, the percentage change in the real oil price can be explained as the percentage quantity change divided by demand elasticity. This however, does only consider changes in quantity (Q) stemming from supply shifts and movements along the demand curve. As pointed out by Hamilton (2009), the demand curve may also shift from year to year on the basis of a number of factors such as increase in real income, population growth and composition, and price of related goods. Hence, any change in the total quantity in any given year can be divided into two parts: One part stemming from shifts in the supply curve and movements along the demand curve (as shown in the previous calculations), and one part stemming from shifts in the demand curve and movements along the supply curve. We also incorporate the scarcity rent by including the relationship described in Eq. (4). The total price change between t and t+1 occurring from all these movements can then be calculated as follows DPt+1 = Pt
DQS,t+1
It is also sometimes termed as user cost or royalty.
/Qt
+
DQD,t+1
eS
/Qt
+ DPt+1 ,
(7)
where DQ S,t +1 and DQ D,t+1 are the quantity change from year t to year t + 1 due to supply and demand changes, respectively. The price elasticity of supply and demand is denoted eD and eS , and DP is the change in the scarcity rent from year t to year t + 1.20 A natural question then arises: How much of any given quantity change is due to shifts in the supply curve and movements along the demand curve, and vice versa? Obviously, there is no way of calculating this fraction with exact precision at any point in time, but due to the non-renewable nature of this commodity it is probably reasonable to assume that long term quantity changes are mainly supply driven. This may change over time however, and our model can easily be used to simulate prices based on various scenarios of this aspect. To estimate the total traded quantity (produced/consumed) at each point in time, Q(t), we use a production function based on the Cauchy distribution Q(t) =
19
eD
(4)
According to Hamilton (2009) the initial price P0 is determined by the transversality condition such that if the price Pt follows Eq. (3) from the starting point, the oil (in our case) is just exhausted at time t = ∞. However, Farzin (1992) points out that the literature contains contrasting views on the subject and argues that the scarcity rent does not need to be neither constant, nor monotonic over time.
(6)
1+
c t−PEAKyear s
2 ,
(8)
where c is the maximum yearly production occurring in year PEAKyear . The parameter s is adjusting the scale of the curve. Preliminary analyses of the total world production suggest that s ∼ 40 provides good results. The model is fairly similar to the initial model
20
These variables may also change over time. We will return to this in Section 5.
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Fig. 4. Illustration of four possible price elasticities of demand (left panel) and supply (right panel). Upper panel: constant elasticity for demand (− 0.2) and supply (0.4). Upper middle panel: discrete changes every 30 years for both demand and supply elasticities. Lower middle panel: continuous changes over time from elastic to inelastic back to (somewhat) elastic. Supply elasticity continuously 0.20 above demand elasticity at all times (difference in absolute value). Lower panel: continuous changes over time from elastic to inelastic. Supply elasticity continuously 0.40 above demand elasticity at all times (difference in absolute value).
of Hubbert (1956), though our approach differs somewhat in the tails of the curve. We have done the same estimations using a logistic production function as well and the results are very similar. In fact, any production curve could be used to evaluate the impact on real prices. The reason is that the model needs only the change in production from one year to another, the fraction of the change stemming from supply shifts and demand shifts, and the price elasticity of supply and demand, to estimate the price change. Innovations in extraction methods and technology in general can also be accounted for by implementing a skewed distribution. Due to the difficulty associated with assessing the amount of remaining oil resources at each point in time t, we use various values for (i) peak year (2015, 2020, 2030) and (ii) various values for the daily maximum production (92 Mbbl/d, 100 Mbbl/d, and 110 Mbbl/d) in our long-term oil price projections.21 ,22 We believe that these values are realistic as Hallock et al. (2014) report simulated peak dates for conventional oil ranging between 2004 and 2051, and their model scenario most consistent with the observed data uses low values for the so called EUR (extractable ultimate resource).23
21
Mbbl = mega barrels (thousand barrels). We have developed a web application where the reader can change the assumptions underlying the analysis and see the results immediately. This application is available at: http://188.166.9.72:3838/users/erikh/ltopSim/. 23 Though their model does not account for the unconventional oil from the U.S. and Canada that seemingly has changed some of the energy landscape. We thank an anonymously referee for pointing this out.
The in-sample fit of the production model is illustrated in Fig. 5. In this figure we use the location parameter 2015 (peak year), a scale parameter of 40, and a maximum daily oil production of 92 Mbbl/d. The figure shows a reasonable long-term fit to the actual production over time, but properties of the production curve can easily be adjusted for subsequent analyses. 5. Results 5.1. In-sample results The in-sample fit from applying the model presented in Eq. (7) is presented in Fig. 6. In this figure, we use the same values for supply and demand elasticities as in Fig. 4 and include scarcity rent values from 4.60% to 5.05%.24 We also plot the corresponding price paths by just using the relationship between supply and demand elasticities (dashed line). While it seems like we are able to capture the long term price patterns for most of the first part of the sample period, the fundamental relationship between supply and demand is not enough when trying to understand the long term oil prices for the later periods. I.e., when accounting for the scarcity rent, the in-sample fit improves substantially. All combinations of values of the scarcity rent and supply and demand elasticities, provide
22
24 The average of the yearly interest rate from Bank of England in the period 1861–2013 is 4.9%, and Jones (2002) reports an average risk free rate for the period 1900–2001 of 4.66% for the U.S. market. We therefore believe that the proposed range is adequate for illustrating the long term effects.
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Fig. 5. Historical world oil production from BP (bold solid line) and EIA (dashed line) and estimated values using Eq. (8). The estimated curve uses a maximum daily oil production of 92 Mbbl/d, a location parameter of 2015 (peak year) and a scale parameter of 40 years.
reasonable fits to the actual historical long term prices, but the results presented in the upper panel seem best by just visual inspection. This is also confirmed by calculations of the mean absolute error. The model estimates that include the scarcity rent in the upper panel have mean absolute errors of $15.7 (left panel) and $14.8, respectively. The corresponding numbers for the lower panel are $23.4 (left) and $16.4 (right). For the estimates without the scarcity rent, the mean absolute errors are ranging from $20.7 to $27.6. Hence, our in-sample results confirm the statement of Hamilton (2009) that “the scarcity rent may have been negligible for previous generations but may now be becoming relevant”. (Hamilton, 2009, p. 180).25 5.2. Out-of-sample scenarios Fig. 7 presents out-of-sample scenarios for the relationship between elasticity levels and real oil prices. The projections for the oil price are constructed by first calculating the year t + n production level with the function provided in Eq. (8). We present price projections based on three production scenarios in this section.26 The first is based on a peak production of 92,000 bbl/d in 2015 (upper panel). The second is based on a peak production level of 100,000 bbl/d in 2020 (middle panel). The third assumes a peak production of 110,000 bbl/d in 2030. The yearly price changes are then calculated using the formula in Eq. (7). For simplicity, we assume that all quantity changes are due to supply shifts and we let the price elasticity of demand vary between −0.20 and −1.1. Hence, the figure illustrates potential future yearly price scenarios also for elasticity levels outside current estimates reported in the academic literature. For the price levels projections (right panel) we start with a price of 55.61 which is the official estimate provided by the EIA for 2015. The reported numbers in Fig. 7 are calculated without taking the scarcity rent into account.27 Focus first on the upper left panel of Fig. 7 (year of peak = 2015). In this case, if price elasticity of demand is at its current “inelastic” estimate level (approximately −0.20) the yearly real price change
25 The in-sample long term price pattern obtained from our model can also be related to the EROI measure presented in Section 2. In the beginning, EROI improves with experience (falling prices) before scarcity induces higher extraction costs which outweigh the gains from improved experience. We are indebted to an anonymous referee for pointing this out and refer interested readers to the work of King et al. (2015) and Guilford et al. (2011). 26 We have developed a web-Appendix where we let the readers change the values of the input variables. We present illustrations of this in the next subsection. 27 We present real price forecasts both with and without the scarcity rent in the next section.
will reach 2.78% in year 2020, 5.62% in 2025, 8.01% in 2030 and will peak in 2056 (outside the figure) at a yearly price increase of 12.50%. The corresponding real prices are presented in the right panel. In the case of a constant elasticity of −0.2 and a peak production year of 2015, the model predicts a real oil price of more than $400 in 2050 (without taking the scarcity rent into account). If price elasticity of demand will stay fixed at a somewhat “more elastic” level (−0.40) the same numbers for the yearly real price changes would be: 1.4% in 2020, 2.8% in 2025, 4.0% in 2030, and 6.2% in 2056. For a more inelastic demand than those presented in the figure the magnitude of the price changes will be larger in general.28 All the numbers in the upper panel of Fig. 7 are based on the assumption of peak production in 2015. The results for the other scenarios (peak year in 2020 and 2030) are similar though the year of the price increase from the model will occur the first time the year after peak production occur. In the years prior to this, there will be a decline in the prices based on the model estimates without accounting for the scarcity rent. However, if one believes in Hotelling’s principle, a long term term price decline is unlikely even in the case of a peak production year of 2020 or 2030. That is, as seen from Fig. 6, the fraction of the total oil price stemming from the scarcity rent will outweigh any price changes due to the fundamental relationship between supply and demand. 5.3. Real oil price forecasts based on Monte Carlo simulation In this section we present simulation results of the real oil price forecasts. The input variables and the corresponding values used in the simulations study are presented in Table 1. To simulate the number of years until the peak production level is reached we use a Poisson distribution with the parameter k equal to 1, 5, and 15 years (mean and variance). For this distribution, the expected value (i.e., the number of years until peak) is also equal to its variance. This number can be changed interactively by the reader in the webAppendix (k between 1 and 50). |estart | is the starting value of the price elasticity of demand. In our case we set this to −0.25. Based on the findings from previous research, as presented in Section 4, we believe that this is a reasonable number. In the simulations we also allow the price elasticity of demand to increase (in absolute terms) in the future. We set the growth rate for this variable equal to 4% annually. sestart is the standard deviation of |estart |. The value for this variable is set to 0.05. The simulated values for e in a given year
28 Krichene (2002) reports long-run price elasticity estimates of between −0.005 and −0.05.
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Fig. 6. Illustration of four estimates of the real oil price based on various demand elasticities and scarcity rents. The thin solid line is the real historical oil prices. The thick solid line is the model estimates with scarcity rent. The dashed line is the model estimates without the scarcity rent. The combinations of elasticity and scarcity rent are as follows: upper left panel: demand and supply elasticity constant (−0.2 and 0.4, respectively. Corresponds to upper panel of Fig. 4) and scarcity rent of 4.60%. Upper right panel: elasticities following the patterns of upper middle panel of Fig. 4 and scarcity rent of 4.75%. Lower left panel: elasticity estimates based on upper middle panel of Fig. 4 and scarcity rent of 4.90%. Lower right panel: elasticities as of lower panel of Fig. 4 and scarcity rent of 5.05%. The dashed line illustrates the corresponding estimated price without adjusting for scarcity rent.
then follow a lognormal distribution with expectations equal to the negative of the log value of the following expression: |et | = |et−1 |(1 + er ),
(9)
and with standard deviation equal to sestart × period, where period is the number of years into the future. We start the simulations by using the predictions from EIA for the 2015 real oil price of $55.61/bbl. The results of the simulation study are illustrated in Fig. 8. The density in this figure is estimated based on the pairs (price; year). That is, for each year from 2015 to 2050 we simulate 1000 prices based on the inputs from Table 1. The density thus describes the probability of the various prices for each year. The higher the density, the more likely the price.29 The left and right panels of this
29 Due to the time used to update the results, we only run 500 simulations in the web Appendix. The results are almost identical to those presented in Fig. 8.
figure present the results without and with the scarcity rent (of 3%), respectively. Focus on the upper panel of Fig. 8 where the expected number of years until peak production occur is set to 1 (k = 1). In this case the median simulated real real oil price is $107/bbl for 2040 and $143/bbl for 2050, without taking the scarcity rent into account. The corresponding numbers when including the scarcity rent are $219/bbl and $392/bbl, respectively. The density clearly illustrates the effect of increased uncertainty when moving further into the future. The middle panel of Fig. 8 illustrates the results using k = 5, and is possibly the most realistic case in terms of findings in previous research. The simulations use the same values for the other input variables as done in the upper panel. In this case, the median simulated prices for 2040 and 2050 are $82/bbl and $108/bbl without including the scarcity rent, respectively. With an annual scarcity rent of 3% the corresponding numbers are $170/bbl in 2040 and $299/bbl in 2050. The lower panel depicts the results with k = 15. In this situation median simulated real oil price for 2040 is $42/bbl, and $49/bbl
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Fig. 7. Examples of the relationship between elasticity levels and long-term oil price developments for various peak years and production levels. Upper panel; peak production = 92,000 bbl/d in 2015. Middle panel; peak production = 100,000 bbl/d in 2020. Lower panel; 110,000 bbl/d in 2030.
in 2050, without controlling for the scarcity rent. Corresponding real oil prices with a scarcity rent of 3% are $85/bbl and $138/bbl, respectively. For comparison, the official outlook from EIA suggests a real oil price of $79/bbl in 2020, $105/bbl in 2030, and $141/bbl in 2040. All the values of the input variables in Table 1 can be modified interactively by the reader in the web Appendix and the updated results for likely future price paths will be available instantly.
6. Summary, implications and concluding comments In this paper we present a model to estimate long term oil prices. The model builds on the fundamental relationships between supply and demand characteristics. Our results show that we are able to reproduce the long term historical patterns in real oil prices when we include the scarcity rent in the calculations. When the model is used to project future oil price scenarios our results suggest likely yearly
E. Haugom, et al. / Energy Economics 58 (2016) 84–94 Table 1 Description of input variables and corresponding values used in simulations. Input
Description
Values
k |estart | sestart er Qstart Qr P
The expected number of years until peak production occurs The expected absolute price elasticity of demand in 2015 The standard deviation of |estart | The annual increase in |e| (%-growth) The production level in bbl/d for the starting year (2015) The annual growth rate in production until year of peak The annual scarcity rent
{1,5,15} 0.25 0.05 0.04 92,000 0.01 {0.00, 0.03}
price changes of between 1.4% and 12.5% in the coming decades given current estimates of price elasticity of demand (without taking the scarcity rent into account). From our in-sample calculations it seems likely however, that the scarcity rent is becoming more and
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more important, and that this will be the main driver of long term future oil prices. We also carry out a simulation study where the probability of various price level forecasts based on the model is presented. We also provide a web-Appendix where the readers can re-run our simulations and interactively change the values of the input variables of our model. The results from these simulations are comparable to the official forecasts from the EIA when using the most likely values for the input variables. However, when including the scarcity rent, a long term drop in prices is unlikely even in the case of global peak production 15 years into the future. Yearly real oil price changes in the range of 2%–12% will likely lead to substantial structural changes in the oil demand. It is also sometimes referred to so-called “backstop technologies” which would allow alternative energy sources to be infinitely supplied if the oil prices reach a specific threshold. Rehrl and Friedrich (2006), for
Fig. 8. Simulations of long term oil prices with various values for the input variables.
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example, state that an unlimited supply of hydrogen from solar thermal power may in the very long term substitute petrol at about $140/bbl. However, as correctly observed by Hamilton (2009), when prices reached $140/bbl in 2008, it was still not clear whether hydrogen could be used to replace petrol at such a large scale or what other backstop resources might be. Acknowledgments We acknowledge Steinar Veka for the very useful help with the R programming language and useful comments and suggestions in general. We also acknowledge Gudbrand Lien and Robert Hirsch for their very useful and valuable comments to earlier versions of the paper. References Adelman, M.A., 2002. Comment on: R.W. Bentley, “Global oil & gas depletion”. Energy Policy 30, 189–205. Adelman, M.A., 2004. The real oil problem. Regulation 27, 16–21. Bernanke, B.S., Gertler, M., Watson, M., Sims, C.A., Friedman, B.M., 1997. Systematic monetary policy and the effects of oil price. Brook. Pap. Econ. Act. 91–157. Cooper, J.C.B., 2003. Price elasticity of demand for crude oil: estimates for 23 countries. OPEC Rev. 27 (1), 1–8. ISSN 1468-0076. URL http://dx.doi.org/10.1111/14680076.00121. http://dx.doi.org/10.1111/1468-0076.00121. Dahl, C., 1993. A survey of oil demand elasticities for developing countries. OPEC Rev. 17 (4), 399–420. ISSN 1468-0076. URL http://dx.doi.org/10.1111/j.1468-0076. 1993.tb00484.x. http://dx.doi.org/10.1111/j.1468-0076.1993.tb00484.x. Dahl, C.A., 2004. International Energy Markets: Understanding Pricing, Policies, and Profits. PennWell Books. Dasgupta, P., Heal, G., Stiflitx, J.E., 1981, December. The taxation of exhaustible resources. Working Paper 436. National Bureau of Economic Research., URL http://www.nber.org/papers/w0436. Farzin, Y.H., 1992. The time path of scarcity rent in the theory of exhaustible resources. Econ. J. 102 (413), 813–830. URL http://www.jstor.org/stable/2234579. ISSN 00130133, 14680297. Gori, F., Ludovisi, D., Cerritelli, P., 2007. Forecast of oil price and consumption in the short term under three scenarios: parabolic, linear and chaotic behaviour. Energy 32 (7), 1291–1296. ISSN 0360-5442. URL http://www.sciencedirect.com/science/ article/pii/S0360544206001873. http://dx.doi.org/10.1016/j.energy.2006.07.005. Greene, D.L., Hopson, J.L., Li, J., 2006. Have we run out of oil yet? Oil peaking analysis from an optimist’s perspective. Energy Policy 34 (5), 515– 531. ISSN 0301-4215. URL http://www.sciencedirect.com/science/article/pii/ S0301421505003083. http://dx.doi.org/10.1016/j.enpol.2005.11.025. Hong Kong Editorial Board meeting presentations Hong Kong Editorial Board meeting presentations. Guilford, M.C., Hall, C.A., O’ Connor, P., Cleveland, C.J., 2011. A new long term assessment of energy return on investment (EROI) for U.S. oil and gas discovery and production. Sustainability 3 (10), 1866–1887.
Hallock, J.L., Jr., Wu, W., Hall, C.A., Jefferson, M., 2014. Forecasting the limits to the availability and diversity of global conventional oil supply: validation. Energy 64 (0), 130–153. ISSN 0360-5442. URL http://www.sciencedirect.com/science/ article/pii/S0360544213009420. http://dx.doi.org/10.1016/j.energy.2013.10.075. Hamilton, J.D., 2009. Understanding crude oil prices. Energy J. 30 (2), 179–206. URL http://www.iaee.org/en/publications/ejarticle.aspx?id=2310. http://dx.doi. org/10.5547/ISSN0195-6574-EJ-Vol30-No2-9. Hong Kong Editorial Board meeting presentations Hong Kong Editorial Board meeting presentations. Hotelling, H., 1931. The economics of exhaustible resources. J. Polit. Econ. 39, URL http://EconPapers.repec.org/RePEc:ucp:jpolec:v:39:y:1913:p:137. Hubbert, M.K., 1956, June. Nuclear energy and the fossil fuels. Working Paper 95. Shell Development Company, Exploration and Development Division. IEA, 2014. What is the difference between conventional and unconventional oil? Technical Report. Jones, C.M., 2002. A century of stock market liquidity and trading costs. Working Paper. Graduate School of Business Colombia University. Kaminski, V., 2012. Energy Markets. Incisive Media, London, UK. Kerr, R.A., 2011. Peak oil production may already be here. Science 331 (6024), 1510– 1511. URL http://www.sciencemag.org/content/331/6024/1510.short. http://dx. doi.org/10.1126/science.331.6024.1510. Kilian, L., 2008. Exogenous oil supply shocks: how big are they and how much do they matter for the U.S. economy? Rev. Econ. Stat. 90 (2), 216–240. ISSN 00346535. URL https://login.ezproxy.hil.no/login?url=http://search.ebscohost.com/login. aspx?direct=true&db=buh&AN=31860529&site=ehost-live&scope=site. Kilian, L., 2009. Not all oil price shocks are alike: disentangling demand and supply shocks in the crude oil marke. Am. Econ. Rev. 99 (3), 1053–1069. ISSN 00028282. URL http://www.jstor.org/stable/25592494. King, C.W., Maxwell, J.P., Donovan, A., 2015. Comparing world economic and net energy metrics, part 2: total economy expenditure perspective. Energies 8 (11). ISSN 1996-1073.12347. URL http://www.mdpi.com/1996-1073/8/11/ 12347. http://dx.doi.org/10.3390/en81112347. Kjärstad, J., Johnsson, F., 2009. Resources and future supply of oil. Energy Policy 37 (2), 441–464. ISSN 0301-4215. URL http://www.sciencedirect.com/science/ article/pii/S0301421508005259. http://dx.doi.org/10.1016/j.enpol.2008.09.056. Krichene, N., 2002. World crude oil and natural gas: a demand and supply model. Energy Econ. 24 (6), 557–576. ISSN 0140-9883. URL http://www. sciencedirect.com/science/article/pii/S0140988302000610. http://dx.doi.org/10. 1016/S0140-9883(02)00061-0. Maggio, G., Cacciola, G., 2009. A variant of the Hubbert curve for world oil production forecasts. Energy Policy 37 (11), 4761–4770. ISSN 0301-4215. URL http://www. sciencedirect.com/science/article/pii/S0301421509004492. http://dx.doi.org/10. 1016/j.enpol.2009.06.053. Murphy, D.J., Hall, C.A.S., 2010. Year in review — EROI or energy return on (energy) invested. Ann. N. Y. Acad. Sci. 1185 (1), 102–118. ISSN 1749-6632. URL http:// dx.doi.org/10.1111/j.1749-6632.2009.05282.x. http://dx.doi.org/10.1111/j.17496632.2009.05282.x. Petroleum, British, 2014, Jun. Statistical Review of World Energy 2014. available at: http://goo.gl/thx3c5 Pindyck, R.S., 1999. The long-run evolutions of energy prices. Energy J. 20 (2), 1–27. URL http://www.iaee.org/en/publications/ejarticle.aspx?id=1308. http://dx.doi. org/10.5547/ISSN0195-6574-EJ-Vol20-No2-1. Rehrl, T., Friedrich, R., 2006. Modelling long-term oil price and extraction with a Hubbert approach: the {LOPEX} model. Energy Policy 34 (15), 2413– 2428. ISSN 0301-4215. URL http://www.sciencedirect.com/science/article/pii/ S0301421505000984. http://dx.doi.org/10.1016/j.enpol.2005.03.021.
Contents lists available at ScienceDirect
Energy Economics journal homepage: www.elsevier.com/locate/eneco
Long term oil prices Erik Haugom a, * , Ørjan Mydland a , Alois Pichler b a b
Lillehammer University College, Lillehammer, Norway The Norwegian University of Science and Technology, Trondheim, Norway
A R T I C L E
I N F O
Article history: Received 19 August 2015 Received in revised form 30 March 2016 Accepted 29 June 2016 Available online 12 July 2016 JEL Classification: A12 C00 E27 E37
A B S T R A C T In this paper we propose a model to estimate and simulate long term oil prices. Our model is based on properties of demand and supply for oil and it is able to reproduce historical real oil prices well. We use the model to estimate and simulate future real oil price scenarios. The results show that if we are not able to significantly increase demand elasticity, the yearly real oil price change can reach 12% in the years following the peak production level without taking a scarcity rent into account. Until peak production level is reached, the long term real oil price changes stemming from fundamental supply and demand changes are expected to be negative. Our simulation results based on an expected peak production year of 2020 and a scarcity rent of 3% suggest an expected real crude oil price of $169/bbl in 2040. For comparison the EIA outlook predicts a real oil price of $141/bbl for the same year. We also provide an on-line Appendix that allows the readers to change the assumptions underlying our analysis and see the results immediately. © 2016 Elsevier B.V. All rights reserved.
Keywords: Oil prices Energy dependence Oil supply Price elasticity of demand and supply
1. Introduction Oil price forecasting is known to be a challenging task. The large number of variables affecting the price, the non-linear effects and feedback loops, and all the “unknowns” and uncertainties can quickly compound into very different estimates depending on who you ask. Maybe for this reason the outlooks that are presented and published have been dominantly concerned with the price development for a few years ahead. Only occasionally researchers aim to model oil and other energy prices into the more distant future. Given the importance of crude oil as the world’s primary energy source in general it is crucial to have a good understanding of how these prices behave in the long term, without getting blinded by all the details and noise influencing the short term prices. In this paper we propose a simple model for the long term oil prices that builds on characteristics of demand and supply. The key element in our model is the fundamental relationship between price, quantity and price elasticity. We compare the estimates from our
* Corresponding author. E-mail addresses: [email protected] (E. Haugom), [email protected] (Ø. Mydland), [email protected] (A. Pichler).
http://dx.doi.org/10.1016/j.eneco.2016.06.014 0140-9883/© 2016 Elsevier B.V. All rights reserved.
model with historical data of real oil prices and show that it is able to reproduce the long term price patterns. Long term price movements are quantified and illustrated through a simulation study. The main result based on our model is that the yearly real oil price changes can reach approximately 12% in the years following the peak production level, without taking the scarcity rent into account.1 These estimates are based on the assumption that price elasticity of demand will stay within the current, “inelastic”, estimates reported in the literature and without considering structural shifts in the demand curve itself.2 However, our in-sample results show that the fundamental relationship between supply and demand is only partly able to explain the long term development of the real oil price. That is, when we “control for” a scarcity rent that increases with the rate of interest, the in-sample fit is improved substantially particularly for the last part of the in-sample period. This indicates that the scarcity rent is becoming more and more important in explaining
1 It is important to note that we talk about the long term price changes in the oil price. That is, yearly price changes can be substantially less and above these estimates due to short term conditions occurring on irregular basis. We will elaborate more on this in the subsequent sections. 2 Price elasticity of demand can primarily be increased by investing in alternative energy sources and preferably through renewable energy sources.
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the long term oil price movements. The results from the out-ofsample simulations suggest that the model can be used to obtain reasonable estimates for future real oil prices. For example, given an expected value of five years until peak production and a scarcity rent of 3% implies an expected oil price of $169/bbl in 2040.3 The official forecast from EIA for the same year is $141/bbl. 1.1. This paper is organized as follows Section 2 provides some information about the properties of crude oil and how the oil market has developed over time. Section 3 reviews some of the literature within oil price modeling and forecasting. In Section 4 we present the theoretical economic relationships for supply and demand and propose the model that can be used to understand long term price developments. In Section 5 we show the results of our model and compare them with actual historical data. We also present future oil price scenarios based on the model. Section 6 concludes and provides implications for policy makers and others working within the area of energy security. 2. Crude oil 2.1. Some properties of crude oil Today crude oil is heavily traded all over the world and it is the most important energy commodity both in physical and monetary terms. Crude oil is not a homogeneous product though. How effectively oil can be processed into refined products depends on a number of characteristics such as (i) specific gravity (density), (ii) sulfur content, (iii) total acid number (TAN), (iv) viscosity, and (v) the percentage of vacuum residue (VR).4 A simple, yet useful measure of the energy obtained from various sources is the term energy return on energy investment (EROI), which can be defined as EROI =
Energyoutput . Energyinput
(1)
In Eq. (1), Energyinput is related to all direct and indirect energy used to produce the final product. For example, indirect energy input can be related to production of capital equipment needed for producing a given form of energy (Kaminski, 2012).5 The world’s total oil reserves consist of conventional and unconventional oil. To simplify we can say that whether oil is conventional or unconventional is determined by the cost and complexity in extraction, and thus related to the EROI. Conventional oil has a relatively high EROI, while unconventional oil, such as tar sands oil, has a low EROI. As Greene et al. (2006) point out, the petroleum resources falling into the two categories are likely to evolve over time as technology improves. Fifty years ago, offshore crude oil was considered unconventional, but is today regarded as a conventional resource (Adelman, 2003). The International Energy Agency (IEA) defines the difference between conventional and unconventional oil as follows: “Conventional oil is a category of oil that includes crude oil and natural gas liquids and condensate liquids, which are extracted from natural gas production. Crude oil production in 2011 stood at approximately 70 million barrels per day. Unconventional oil consists of a wider variety of liquid sources including oil sands, extra heavy oil, gas to liquids and other liquids. In general, conventional oil is easier and cheaper to produce than
3 This estimate is based on various values for the other input variables defined in Table 1. 4 See Kaminski (2012) for a description of these characteristics and other properties influencing the overall quality of crude oil and refined products. 5 For a thorough review of the concept of EROI we refer to Murphy and Hall (2010).
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unconventional oil. However, the categories “conventional” and “unconventional” do not remain fixed, and over time, as economic and technological conditions evolve, resources hitherto considered unconventional can migrate into the conventional category.” (IEA, 2014) Whether oil is classified as unconventional or conventional does not affect the geological limits of how much oil is left to be extracted, only how “easy” or “hard” it is to extract the oil that is in the ground using the technology at hand.6 2.2. Oil reserves and production Accurate measures of the oil reserves are important in order to predict future supply and prices. However, different definitions, errors, omissions, double counting, and the dynamic aspect of exploration, make it a very challenging task to obtain good estimates of the total oil reserves at any given point in time. Given the facts about the geological process of “creating” new oil and fossil fuels in general, we can assume with complete assurance, as Hubbert (1956) did, that the total world supply of oil, as we know it, is initially fixed “to which there will be no significant additions during the period of our interest” (Hubbert, 1956, p. 4). This view is not necessarily the same as saying that world oil production is peaking in the near future, only that the total “stock” of oil is fixed due to the extreme lead time. However, in the academic discussion about oil resources and production, there are scholars arguing that limitations of oil resources are irrelevant due to technological change and market forces (see e.g., Adelman, 2004). In this discussion over oil resources and production we can broadly divide the participants into two groups: the “pessimists” and the “optimists”. The former group is interested in peaking curves using similar methods to those of Hubbert (1956) and generally believes that geology will be more important than economics or technology in determining when oil production will peak. The participants of the latter group (the optimists) are often referred to as “economists” because of their strong belief in the market mechanisms and technological innovation (Greene et al., 2006). Our approach falls in between these two viewpoints. We assume that the total worldwide oil reserves are fixed and that the long term supply can be approximated well by using the approach of e.g., Hubbert (1956).7 Then we use the fundamental relationships between supply and demand to evaluate possible future outcomes for the real oil prices. The challenges with estimating the total global oil reserves are not only related to when peak production will occur, but also the availability of historical data. Fig. 1 illustrates historical oil production from two different sources. These numbers date back to 1965 (British Petroleum, BP) and 1980 (Energy Information Agency, EIA), respectively.8 Though these historical numbers from the two sources are fairly similar, the EIA estimates are somewhat larger than the BP estimates. The difference in the 2013 daily production numbers is approximately 3.5 million bbl/d.9 In the long run, such differences, and the eventual short-term ups and downs in the
6 What is currently considered to be the cutting edge technology can be the standard technology in five or ten years, and so forth. However, as new technology also can be very energy intensive a decline in future EROI is still likely. We are thankful to an anonymous referee for pointing this out. 7 Though we use various functions for estimating the total supply of oil, we assume that all the production curves exhibit a common property of beginning at zero and ending at zero after passing through a maxima. We will briefly consider movements in supply related to the OPEC cartel though in the next sub-section. It is important to note that such movements are of relative short term in our framework. 8 Despite an extensive effort, we have not been able to find historical oil production data for a longer time horizon than this. 9 Some of the differences in the production numbers are definitional. We are indebted to an anonymous referee for pointing this out and refer interested readers to Jean Laherrere’s ASPO France webpage (http://aspofrance.org/) for definitions.
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Fig. 1. Historical world oil production from BP (solid line: 1965–2013) and EIA (dashed lined: 1980–2013).
production level, are of minor importance for the purpose of this paper.10 We will return to how we estimate historical and future production levels in Section 4. 2.3. Historical prices Fig. 2 illustrates the development of real oil prices from 1861 to 2013. It is evident from this figure that real oil prices have undergone many small and large changes over time. In the very beginning of the market place for oil, prices were extremely erratic and reached almost 120 $/bbl in real terms (2013 dollars) in 1864. The high volatility of the oil market kept on until the early 1870s. Such a high volatility in the early days of the market is somewhat expected as the uncertainty about supply and demand was high. For the approximately 100 years to follow (1874–1974) the yearly real oil price changes were relatively modest. However, from 1961, when OPEC was established, both the oil price, and the volatility of the oil price, became higher compared with the previous period from around 1880 to 1960.11 It is important to note, however, that we are not particularly interested in understanding the short term shocks or fluctuations in this paper, but rather the long term developments. Therefore, we have superimposed a 25 years moving average (dashed line) and a quadratic trend line (dotted line) in Fig. 2. It is evident from the figure that the long term prices generally fell until the 1940s/1950s, a period during which the production of oil increased rapidly (Pindyck, 1999). From that point onwards, the real prices have generally increased and the quadratic trend line seems to provide a good approximation as pointed out by Pindyck (1999). However, such a trend line does not take into account any information about fundamental aspects of supply and demand, it only uses the properties of historical prices. In the next section we briefly review various approaches researchers have used to understand long term oil prices.
either reserves, future supply or (peak) production (see e.g., Greene et al., 2006; Kjärstad and Johnsson, 2009; Kerr, 2011; Maggio and Cacciola, 2009), taxation (Dasgupta et al., 1981), oil price shocks based on properties of supply and demand (see Kilian, 2008; Kilian, 2009), or (relatively) short term forecasting of oil prices and consumption (see e.g., Gori et al., 2007, and the references therein). One reason for why so little attention has been given to long term oil price modeling and forecasting may be that it is known to be difficult due to ambiguous or poor information about the true global oil resources (Kjärstad and Johnsson, 2009), and the complexity of both the characteristics of the commodity and the market mechanisms in general. One recent study that focuses on understanding the behavior of oil prices is the seminal paper of Hamilton (2009). In this paper Hamilton (2009) explores three broad ways to explain oil price changes: (i) statistical investigation of historical data, (ii) predictions of economic theory, and (iii) fundamental determinants and prospects for demand and supply. The overall conclusion of that paper is that: “. . . the low price-elasticity of short-run demand and supply, the vulnerability of supplies to disruptions, and the peak in U.S. oil production account for the broad behavior of oil prices over 1970–1997. Although the traditional economic theory of exhaustible resources does not fit in an obvious way into this historical account, the profound change in demand coming from the newly industrialized countries and recognition of the finiteness of
3. Modeling and forecasting long term oil prices Despite our great oil dependence, there is remarkably little research aiming at understanding the long term real oil price development. The focus of the research within the oil sector is often on
10 For example, the Organization of Petroleum Exporting Countries (OPEC) was established in 1961 by Saudi Arabia, Iran, Iraq, Kuwait and Venezuela to coordinate oil policy in the member states, and to stabilize price fluctuations. To keep the price at a high level, OPEC has limit their total production and each member country has a production quota. This however, could provide an incentive for the member countries to report higher reserve estimates. 11 For a review of the history of OPEC we refer interested readers to Dahl (2004) and Bernanke et al. (1997).
Fig. 2. Development of real oil prices from 1861–2013 (solid line) and corresponding 25 years moving average (dashed line). The dotted line is a fitted quadratic trend line as done in Pindyck (1999). Base year for the real oil price calculations is 2013. Source: Source: Petroleum (2014).
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this resource offers a plausible explanation for more recent developments. In other words, the scarcity rent may have been negligible for previous generations but may now be becoming relevant. . . ” (Hamilton, 2009, p. 180). Though the paper uses relatively long period of sample data,12 it does not include prices from the early days of the oil market, as we know it. This however is done in Pindyck (1999) who examines real crude oil prices over a 127-year period (1870–1996) using a statistical approach.13 In this study Pindyck (1999) argues that real energy prices, including crude oil prices, is best described by a multivariate stochastic process. That is, the behavior of such prices suggests reversion to trend lines with slopes and levels that are both shifting continuously and unpredictably over time. The approach used in this paper does not include prospects of supply and demand but rely on correlations in historical data.14 One of few recent attempts of modeling long term oil prices and linking this with fundamental information about oil supply is the study by Rehrl and Friedrich (2006). In this paper, they use the framework of Hubbert (1956), but embed profitability and technical progress. Their main finding about the long term oil price is that “. . . a significant higher oil price beyond 2010 is expected in any LOPEX15 scenario, compared to the reference scenario that is currently used for policy decisions . . . Very high oil prices certainly would go along with heavy structural changes on the demand side which is probably not covered in LOPEX”. In our model, we particularly focus on the demand side, and show the long term real oil price scenarios for various properties of supply and demand in particular. 4. Theoretical relationships 4.1. Supply and demand As done in Hubbert (1956) we assume that oil is a resource with an initially fixed supply in the earth to which there will be no significant additions during the period of interest. Hence, at some point in time the production will reach a peak and then eventually start to fall. Such a continuous drop in production may be viewed as negative shifts in the supply. The slope of the demand curves determines the effect on the price from a drop in oil production.16 The steeper the slope of the demand curve the bigger the price increase from a decrease in the supply. The relationship between the steepness of the demand curve and the effect on the price is described well in the seminal paper of (Hamilton, 2009, p. 186). He writes: “. . . suppose we take it as given that as a result of unavoidable geolocial limits, global production of crude oil next year
12 The sample of West Texas Intermediate average monthly prices is for 1947:M1 through 2008:M10. 13 In this paper the long term price characteristics of bituminous coal and natural gas are also examined. 14 The time-varying aspect is accounted for using a Kalman filter. 15 LOPEX is the name of their model. 16 The slope of the supply curve will also have an impact on the price. Previous research reports very inelastic supply of oil (Krichene, 2002). This essentially means that oil producers will extract the available oil without doing too many considerations about the price. This supports the use of production curves for the supply which follows the general pattern of those presented by Hubbert (1956) and others. We recommend the seminal paper of Hallock et al. (2014) and the various references therein when it comes to modeling and forecasting oil production and demand. It is also important to note that the properties of any production curve can be adjusted easily, and it is not the objective of this paper to find the best possible production curve for crude oil, but rather illustrate how our model can be used for various production curves and prospects for demand characteristics.
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could only be 90% of the amount being produced this year. If we assumed say a short-run demand price elasticity of −0.10, that would imply a price of oil next year that is twice its current value.” Fig. 3 illustrates this principle by depicting price as a function of price elasticity of demand and the magnitude of the supply change. Using the example of Hamilton (2009), where global oil production drops 10% next year and the short-term demand price elasticity is −0.10, the figure shows that the price of oil next year will be twice its current value. For a more elastic demand curve (e.g., −0.50) the impact on prices will be substantially less for the same reduction in supply. The principle of prices exceeding marginal production costs for exhaustible resources even if the market is perfectly competitive dates back to Harold Hotelling in 1931 (Hotelling, 1931). We will return to this important aspect in Section 4.2. Price elasticities of supply and demand are crucial inputs in our analytical model. Several studies have estimated these quantities using various approaches. Cooper (2003) estimated the long-run price elasticity of demand to be −0.21 by using an annual time series regression on averages of 23 countries, while Dahl (1993) reports a long-run price elasticity of demand estimate of −0.30 for developing countries by doing a literature survey. Krichene (2002) reports price elasticity of demand of between −0.005 (1973–1999) and −0.13 (1918–1973) using a cointegration approach. By examining previous empirical research Hamilton (2009) reports findings of the short-run elasticity of gasoline to be around −0.25 and a long-run elasticity 2 or 3 times as large. The corresponding numbers for oil are ranging from −0.05 to −0.07 in the short-run to −0.21 to −0.30 in the long-run. Indirectly, all these findings suggest that we are extremely oil-dependent. If the price of oil increases we will hardly use any less oil at all. Put differently; the slope of the demand curve is very steep, and a negative shift in the supply curve will have a big impact on the price. Corresponding supply elasticities are generally found to be larger (in absolute terms), and Krichene (2002) reports estimates of between 0.10 (1973–1999) and 1.10 (1918–1973) for this quantity. In general, he finds that both supply and demand have become more inelastic (approaching zero) over the years.17 However, as both the demand curve and supply curve are shifting due to a number of factors, it is very challenging to obtain precise estimates of elasticities in general. In the analyses we will therefore show the results using a range of numbers for the price elasticity of demand and supply.18 Fig. 4 illustrates four different paths for the price elasticity of supply and demand over time. In the upper panel the elasticities are constant across the whole period (−0.2 for the demand elasticity and 0.4 for the corresponding supply elasticity). In the lower three panels we let both elasticities change over time. The upper middle panel reflects discrete changes where price elasticity of both demand and supply becomes more inelastic over time. The lower middle panel depicts a case where both demand and supply elasticity are changing continuously, from (relatively) elastic to inelastic and back to (somewhat) elastic into the future. Such a pattern would reflect a situation where we are able to be less “oil dependent” in the future. The lower panel is identical to the lower middle panel but the starting point for the two elasticities is different. In this case the price elasticity of demand is more inelastic at all times, while the price elasticity of supply is more elastic. Hence, the difference between the two (the difference in absolute value) is increased compared to the numbers in the panel above. All the numbers for the price elasticities for
17 Krichene (2002) uses data on world crude oil output and prices from 1918–1999 to estimate supply and demand elasticities. 18 If utilizing publicly available crude oil price/production forecasts, the model can be used to estimate elasticities implied by the forecasts. This is beyond the scope of this paper, but is certainly an interesting direction for future work.
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For our purpose, the functional form of the scarcity rent over time is of minor importance. The relevant point is that the fundamental theoretical relationship between supply and demand and principles about the scarcity rent can be combined in one model framework. In the next section we present our analytical model. This model will then be evaluated in-sample before we show out-of-sample scenarios using various properties of supply and demand. 4.3. Analytical model The basic assumption is the relationship e :=
P Q
•
DQ , DP
(5)
where the quantity e is the price elasticity of demand. By reallocating this formula we can also write the relationship as DP 1 DQ = • . P e Q Fig. 3. Yearly oil price change as a function of production change and price elasticity of demand.
both supply and demand are within, or close to, the range of previous findings in the literature as mentioned above. Demand may also shift due external factors such as real income, price of related goods, population size and composition. Hence, as pointed out by Hamilton (2009), both the demand curve and the supply curve are shifting as a result of a number of factors in any given year. Even though it may be hard to come up with an exact number of how much of the quantity change from one year to another that is supply and demand driven, our model is able to take such movements, and elasticities of both supply and demand, into account. This will be elaborated in Section 4.3. 4.2. Scarcity rent The use of one barrel of oil today implies that this barrel cannot be used at any point in the future — it is gone forever. This “lost future benefit” can be measured as a cost. This cost is commonly referred to as the scarcity rent, and it dates back to Harold Hotelling in 1931.19 Formally, the scarcity rent, P t , can be defined as the difference between the price Pt and the marginal production cost Mt : Pt = Pt − Mt .
(2)
According to Hotelling’s principle, the scarcity rent should then rise at the rate of interest as shown in Hamilton (2009), such that Pt+1 − Mt+1 = (1 + i)(Pt − Mt ).
(3)
Using the relationship in Eq. (2) we can express the scarcity rent as Pt+1 = (1 + i)(Pt ).
That is, the percentage change in the real oil price can be explained as the percentage quantity change divided by demand elasticity. This however, does only consider changes in quantity (Q) stemming from supply shifts and movements along the demand curve. As pointed out by Hamilton (2009), the demand curve may also shift from year to year on the basis of a number of factors such as increase in real income, population growth and composition, and price of related goods. Hence, any change in the total quantity in any given year can be divided into two parts: One part stemming from shifts in the supply curve and movements along the demand curve (as shown in the previous calculations), and one part stemming from shifts in the demand curve and movements along the supply curve. We also incorporate the scarcity rent by including the relationship described in Eq. (4). The total price change between t and t+1 occurring from all these movements can then be calculated as follows DPt+1 = Pt
DQS,t+1
It is also sometimes termed as user cost or royalty.
/Qt
+
DQD,t+1
eS
/Qt
+ DPt+1 ,
(7)
where DQ S,t +1 and DQ D,t+1 are the quantity change from year t to year t + 1 due to supply and demand changes, respectively. The price elasticity of supply and demand is denoted eD and eS , and DP is the change in the scarcity rent from year t to year t + 1.20 A natural question then arises: How much of any given quantity change is due to shifts in the supply curve and movements along the demand curve, and vice versa? Obviously, there is no way of calculating this fraction with exact precision at any point in time, but due to the non-renewable nature of this commodity it is probably reasonable to assume that long term quantity changes are mainly supply driven. This may change over time however, and our model can easily be used to simulate prices based on various scenarios of this aspect. To estimate the total traded quantity (produced/consumed) at each point in time, Q(t), we use a production function based on the Cauchy distribution Q(t) =
19
eD
(4)
According to Hamilton (2009) the initial price P0 is determined by the transversality condition such that if the price Pt follows Eq. (3) from the starting point, the oil (in our case) is just exhausted at time t = ∞. However, Farzin (1992) points out that the literature contains contrasting views on the subject and argues that the scarcity rent does not need to be neither constant, nor monotonic over time.
(6)
1+
c t−PEAKyear s
2 ,
(8)
where c is the maximum yearly production occurring in year PEAKyear . The parameter s is adjusting the scale of the curve. Preliminary analyses of the total world production suggest that s ∼ 40 provides good results. The model is fairly similar to the initial model
20
These variables may also change over time. We will return to this in Section 5.
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Fig. 4. Illustration of four possible price elasticities of demand (left panel) and supply (right panel). Upper panel: constant elasticity for demand (− 0.2) and supply (0.4). Upper middle panel: discrete changes every 30 years for both demand and supply elasticities. Lower middle panel: continuous changes over time from elastic to inelastic back to (somewhat) elastic. Supply elasticity continuously 0.20 above demand elasticity at all times (difference in absolute value). Lower panel: continuous changes over time from elastic to inelastic. Supply elasticity continuously 0.40 above demand elasticity at all times (difference in absolute value).
of Hubbert (1956), though our approach differs somewhat in the tails of the curve. We have done the same estimations using a logistic production function as well and the results are very similar. In fact, any production curve could be used to evaluate the impact on real prices. The reason is that the model needs only the change in production from one year to another, the fraction of the change stemming from supply shifts and demand shifts, and the price elasticity of supply and demand, to estimate the price change. Innovations in extraction methods and technology in general can also be accounted for by implementing a skewed distribution. Due to the difficulty associated with assessing the amount of remaining oil resources at each point in time t, we use various values for (i) peak year (2015, 2020, 2030) and (ii) various values for the daily maximum production (92 Mbbl/d, 100 Mbbl/d, and 110 Mbbl/d) in our long-term oil price projections.21 ,22 We believe that these values are realistic as Hallock et al. (2014) report simulated peak dates for conventional oil ranging between 2004 and 2051, and their model scenario most consistent with the observed data uses low values for the so called EUR (extractable ultimate resource).23
21
Mbbl = mega barrels (thousand barrels). We have developed a web application where the reader can change the assumptions underlying the analysis and see the results immediately. This application is available at: http://188.166.9.72:3838/users/erikh/ltopSim/. 23 Though their model does not account for the unconventional oil from the U.S. and Canada that seemingly has changed some of the energy landscape. We thank an anonymously referee for pointing this out.
The in-sample fit of the production model is illustrated in Fig. 5. In this figure we use the location parameter 2015 (peak year), a scale parameter of 40, and a maximum daily oil production of 92 Mbbl/d. The figure shows a reasonable long-term fit to the actual production over time, but properties of the production curve can easily be adjusted for subsequent analyses. 5. Results 5.1. In-sample results The in-sample fit from applying the model presented in Eq. (7) is presented in Fig. 6. In this figure, we use the same values for supply and demand elasticities as in Fig. 4 and include scarcity rent values from 4.60% to 5.05%.24 We also plot the corresponding price paths by just using the relationship between supply and demand elasticities (dashed line). While it seems like we are able to capture the long term price patterns for most of the first part of the sample period, the fundamental relationship between supply and demand is not enough when trying to understand the long term oil prices for the later periods. I.e., when accounting for the scarcity rent, the in-sample fit improves substantially. All combinations of values of the scarcity rent and supply and demand elasticities, provide
22
24 The average of the yearly interest rate from Bank of England in the period 1861–2013 is 4.9%, and Jones (2002) reports an average risk free rate for the period 1900–2001 of 4.66% for the U.S. market. We therefore believe that the proposed range is adequate for illustrating the long term effects.
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Fig. 5. Historical world oil production from BP (bold solid line) and EIA (dashed line) and estimated values using Eq. (8). The estimated curve uses a maximum daily oil production of 92 Mbbl/d, a location parameter of 2015 (peak year) and a scale parameter of 40 years.
reasonable fits to the actual historical long term prices, but the results presented in the upper panel seem best by just visual inspection. This is also confirmed by calculations of the mean absolute error. The model estimates that include the scarcity rent in the upper panel have mean absolute errors of $15.7 (left panel) and $14.8, respectively. The corresponding numbers for the lower panel are $23.4 (left) and $16.4 (right). For the estimates without the scarcity rent, the mean absolute errors are ranging from $20.7 to $27.6. Hence, our in-sample results confirm the statement of Hamilton (2009) that “the scarcity rent may have been negligible for previous generations but may now be becoming relevant”. (Hamilton, 2009, p. 180).25 5.2. Out-of-sample scenarios Fig. 7 presents out-of-sample scenarios for the relationship between elasticity levels and real oil prices. The projections for the oil price are constructed by first calculating the year t + n production level with the function provided in Eq. (8). We present price projections based on three production scenarios in this section.26 The first is based on a peak production of 92,000 bbl/d in 2015 (upper panel). The second is based on a peak production level of 100,000 bbl/d in 2020 (middle panel). The third assumes a peak production of 110,000 bbl/d in 2030. The yearly price changes are then calculated using the formula in Eq. (7). For simplicity, we assume that all quantity changes are due to supply shifts and we let the price elasticity of demand vary between −0.20 and −1.1. Hence, the figure illustrates potential future yearly price scenarios also for elasticity levels outside current estimates reported in the academic literature. For the price levels projections (right panel) we start with a price of 55.61 which is the official estimate provided by the EIA for 2015. The reported numbers in Fig. 7 are calculated without taking the scarcity rent into account.27 Focus first on the upper left panel of Fig. 7 (year of peak = 2015). In this case, if price elasticity of demand is at its current “inelastic” estimate level (approximately −0.20) the yearly real price change
25 The in-sample long term price pattern obtained from our model can also be related to the EROI measure presented in Section 2. In the beginning, EROI improves with experience (falling prices) before scarcity induces higher extraction costs which outweigh the gains from improved experience. We are indebted to an anonymous referee for pointing this out and refer interested readers to the work of King et al. (2015) and Guilford et al. (2011). 26 We have developed a web-Appendix where we let the readers change the values of the input variables. We present illustrations of this in the next subsection. 27 We present real price forecasts both with and without the scarcity rent in the next section.
will reach 2.78% in year 2020, 5.62% in 2025, 8.01% in 2030 and will peak in 2056 (outside the figure) at a yearly price increase of 12.50%. The corresponding real prices are presented in the right panel. In the case of a constant elasticity of −0.2 and a peak production year of 2015, the model predicts a real oil price of more than $400 in 2050 (without taking the scarcity rent into account). If price elasticity of demand will stay fixed at a somewhat “more elastic” level (−0.40) the same numbers for the yearly real price changes would be: 1.4% in 2020, 2.8% in 2025, 4.0% in 2030, and 6.2% in 2056. For a more inelastic demand than those presented in the figure the magnitude of the price changes will be larger in general.28 All the numbers in the upper panel of Fig. 7 are based on the assumption of peak production in 2015. The results for the other scenarios (peak year in 2020 and 2030) are similar though the year of the price increase from the model will occur the first time the year after peak production occur. In the years prior to this, there will be a decline in the prices based on the model estimates without accounting for the scarcity rent. However, if one believes in Hotelling’s principle, a long term term price decline is unlikely even in the case of a peak production year of 2020 or 2030. That is, as seen from Fig. 6, the fraction of the total oil price stemming from the scarcity rent will outweigh any price changes due to the fundamental relationship between supply and demand. 5.3. Real oil price forecasts based on Monte Carlo simulation In this section we present simulation results of the real oil price forecasts. The input variables and the corresponding values used in the simulations study are presented in Table 1. To simulate the number of years until the peak production level is reached we use a Poisson distribution with the parameter k equal to 1, 5, and 15 years (mean and variance). For this distribution, the expected value (i.e., the number of years until peak) is also equal to its variance. This number can be changed interactively by the reader in the webAppendix (k between 1 and 50). |estart | is the starting value of the price elasticity of demand. In our case we set this to −0.25. Based on the findings from previous research, as presented in Section 4, we believe that this is a reasonable number. In the simulations we also allow the price elasticity of demand to increase (in absolute terms) in the future. We set the growth rate for this variable equal to 4% annually. sestart is the standard deviation of |estart |. The value for this variable is set to 0.05. The simulated values for e in a given year
28 Krichene (2002) reports long-run price elasticity estimates of between −0.005 and −0.05.
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Fig. 6. Illustration of four estimates of the real oil price based on various demand elasticities and scarcity rents. The thin solid line is the real historical oil prices. The thick solid line is the model estimates with scarcity rent. The dashed line is the model estimates without the scarcity rent. The combinations of elasticity and scarcity rent are as follows: upper left panel: demand and supply elasticity constant (−0.2 and 0.4, respectively. Corresponds to upper panel of Fig. 4) and scarcity rent of 4.60%. Upper right panel: elasticities following the patterns of upper middle panel of Fig. 4 and scarcity rent of 4.75%. Lower left panel: elasticity estimates based on upper middle panel of Fig. 4 and scarcity rent of 4.90%. Lower right panel: elasticities as of lower panel of Fig. 4 and scarcity rent of 5.05%. The dashed line illustrates the corresponding estimated price without adjusting for scarcity rent.
then follow a lognormal distribution with expectations equal to the negative of the log value of the following expression: |et | = |et−1 |(1 + er ),
(9)
and with standard deviation equal to sestart × period, where period is the number of years into the future. We start the simulations by using the predictions from EIA for the 2015 real oil price of $55.61/bbl. The results of the simulation study are illustrated in Fig. 8. The density in this figure is estimated based on the pairs (price; year). That is, for each year from 2015 to 2050 we simulate 1000 prices based on the inputs from Table 1. The density thus describes the probability of the various prices for each year. The higher the density, the more likely the price.29 The left and right panels of this
29 Due to the time used to update the results, we only run 500 simulations in the web Appendix. The results are almost identical to those presented in Fig. 8.
figure present the results without and with the scarcity rent (of 3%), respectively. Focus on the upper panel of Fig. 8 where the expected number of years until peak production occur is set to 1 (k = 1). In this case the median simulated real real oil price is $107/bbl for 2040 and $143/bbl for 2050, without taking the scarcity rent into account. The corresponding numbers when including the scarcity rent are $219/bbl and $392/bbl, respectively. The density clearly illustrates the effect of increased uncertainty when moving further into the future. The middle panel of Fig. 8 illustrates the results using k = 5, and is possibly the most realistic case in terms of findings in previous research. The simulations use the same values for the other input variables as done in the upper panel. In this case, the median simulated prices for 2040 and 2050 are $82/bbl and $108/bbl without including the scarcity rent, respectively. With an annual scarcity rent of 3% the corresponding numbers are $170/bbl in 2040 and $299/bbl in 2050. The lower panel depicts the results with k = 15. In this situation median simulated real oil price for 2040 is $42/bbl, and $49/bbl
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Fig. 7. Examples of the relationship between elasticity levels and long-term oil price developments for various peak years and production levels. Upper panel; peak production = 92,000 bbl/d in 2015. Middle panel; peak production = 100,000 bbl/d in 2020. Lower panel; 110,000 bbl/d in 2030.
in 2050, without controlling for the scarcity rent. Corresponding real oil prices with a scarcity rent of 3% are $85/bbl and $138/bbl, respectively. For comparison, the official outlook from EIA suggests a real oil price of $79/bbl in 2020, $105/bbl in 2030, and $141/bbl in 2040. All the values of the input variables in Table 1 can be modified interactively by the reader in the web Appendix and the updated results for likely future price paths will be available instantly.
6. Summary, implications and concluding comments In this paper we present a model to estimate long term oil prices. The model builds on the fundamental relationships between supply and demand characteristics. Our results show that we are able to reproduce the long term historical patterns in real oil prices when we include the scarcity rent in the calculations. When the model is used to project future oil price scenarios our results suggest likely yearly
E. Haugom, et al. / Energy Economics 58 (2016) 84–94 Table 1 Description of input variables and corresponding values used in simulations. Input
Description
Values
k |estart | sestart er Qstart Qr P
The expected number of years until peak production occurs The expected absolute price elasticity of demand in 2015 The standard deviation of |estart | The annual increase in |e| (%-growth) The production level in bbl/d for the starting year (2015) The annual growth rate in production until year of peak The annual scarcity rent
{1,5,15} 0.25 0.05 0.04 92,000 0.01 {0.00, 0.03}
price changes of between 1.4% and 12.5% in the coming decades given current estimates of price elasticity of demand (without taking the scarcity rent into account). From our in-sample calculations it seems likely however, that the scarcity rent is becoming more and
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more important, and that this will be the main driver of long term future oil prices. We also carry out a simulation study where the probability of various price level forecasts based on the model is presented. We also provide a web-Appendix where the readers can re-run our simulations and interactively change the values of the input variables of our model. The results from these simulations are comparable to the official forecasts from the EIA when using the most likely values for the input variables. However, when including the scarcity rent, a long term drop in prices is unlikely even in the case of global peak production 15 years into the future. Yearly real oil price changes in the range of 2%–12% will likely lead to substantial structural changes in the oil demand. It is also sometimes referred to so-called “backstop technologies” which would allow alternative energy sources to be infinitely supplied if the oil prices reach a specific threshold. Rehrl and Friedrich (2006), for
Fig. 8. Simulations of long term oil prices with various values for the input variables.
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example, state that an unlimited supply of hydrogen from solar thermal power may in the very long term substitute petrol at about $140/bbl. However, as correctly observed by Hamilton (2009), when prices reached $140/bbl in 2008, it was still not clear whether hydrogen could be used to replace petrol at such a large scale or what other backstop resources might be. Acknowledgments We acknowledge Steinar Veka for the very useful help with the R programming language and useful comments and suggestions in general. We also acknowledge Gudbrand Lien and Robert Hirsch for their very useful and valuable comments to earlier versions of the paper. References Adelman, M.A., 2002. Comment on: R.W. Bentley, “Global oil & gas depletion”. Energy Policy 30, 189–205. Adelman, M.A., 2004. The real oil problem. Regulation 27, 16–21. Bernanke, B.S., Gertler, M., Watson, M., Sims, C.A., Friedman, B.M., 1997. Systematic monetary policy and the effects of oil price. Brook. Pap. Econ. Act. 91–157. Cooper, J.C.B., 2003. Price elasticity of demand for crude oil: estimates for 23 countries. OPEC Rev. 27 (1), 1–8. ISSN 1468-0076. URL http://dx.doi.org/10.1111/14680076.00121. http://dx.doi.org/10.1111/1468-0076.00121. Dahl, C., 1993. A survey of oil demand elasticities for developing countries. OPEC Rev. 17 (4), 399–420. ISSN 1468-0076. URL http://dx.doi.org/10.1111/j.1468-0076. 1993.tb00484.x. http://dx.doi.org/10.1111/j.1468-0076.1993.tb00484.x. Dahl, C.A., 2004. International Energy Markets: Understanding Pricing, Policies, and Profits. PennWell Books. Dasgupta, P., Heal, G., Stiflitx, J.E., 1981, December. The taxation of exhaustible resources. Working Paper 436. National Bureau of Economic Research., URL http://www.nber.org/papers/w0436. Farzin, Y.H., 1992. The time path of scarcity rent in the theory of exhaustible resources. Econ. J. 102 (413), 813–830. URL http://www.jstor.org/stable/2234579. ISSN 00130133, 14680297. Gori, F., Ludovisi, D., Cerritelli, P., 2007. Forecast of oil price and consumption in the short term under three scenarios: parabolic, linear and chaotic behaviour. Energy 32 (7), 1291–1296. ISSN 0360-5442. URL http://www.sciencedirect.com/science/ article/pii/S0360544206001873. http://dx.doi.org/10.1016/j.energy.2006.07.005. Greene, D.L., Hopson, J.L., Li, J., 2006. Have we run out of oil yet? Oil peaking analysis from an optimist’s perspective. Energy Policy 34 (5), 515– 531. ISSN 0301-4215. URL http://www.sciencedirect.com/science/article/pii/ S0301421505003083. http://dx.doi.org/10.1016/j.enpol.2005.11.025. Hong Kong Editorial Board meeting presentations Hong Kong Editorial Board meeting presentations. Guilford, M.C., Hall, C.A., O’ Connor, P., Cleveland, C.J., 2011. A new long term assessment of energy return on investment (EROI) for U.S. oil and gas discovery and production. Sustainability 3 (10), 1866–1887.
Hallock, J.L., Jr., Wu, W., Hall, C.A., Jefferson, M., 2014. Forecasting the limits to the availability and diversity of global conventional oil supply: validation. Energy 64 (0), 130–153. ISSN 0360-5442. URL http://www.sciencedirect.com/science/ article/pii/S0360544213009420. http://dx.doi.org/10.1016/j.energy.2013.10.075. Hamilton, J.D., 2009. Understanding crude oil prices. Energy J. 30 (2), 179–206. URL http://www.iaee.org/en/publications/ejarticle.aspx?id=2310. http://dx.doi. org/10.5547/ISSN0195-6574-EJ-Vol30-No2-9. Hong Kong Editorial Board meeting presentations Hong Kong Editorial Board meeting presentations. Hotelling, H., 1931. The economics of exhaustible resources. J. Polit. Econ. 39, URL http://EconPapers.repec.org/RePEc:ucp:jpolec:v:39:y:1913:p:137. Hubbert, M.K., 1956, June. Nuclear energy and the fossil fuels. Working Paper 95. Shell Development Company, Exploration and Development Division. IEA, 2014. What is the difference between conventional and unconventional oil? Technical Report. Jones, C.M., 2002. A century of stock market liquidity and trading costs. Working Paper. Graduate School of Business Colombia University. Kaminski, V., 2012. Energy Markets. Incisive Media, London, UK. Kerr, R.A., 2011. Peak oil production may already be here. Science 331 (6024), 1510– 1511. URL http://www.sciencemag.org/content/331/6024/1510.short. http://dx. doi.org/10.1126/science.331.6024.1510. Kilian, L., 2008. Exogenous oil supply shocks: how big are they and how much do they matter for the U.S. economy? Rev. Econ. Stat. 90 (2), 216–240. ISSN 00346535. URL https://login.ezproxy.hil.no/login?url=http://search.ebscohost.com/login. aspx?direct=true&db=buh&AN=31860529&site=ehost-live&scope=site. Kilian, L., 2009. Not all oil price shocks are alike: disentangling demand and supply shocks in the crude oil marke. Am. Econ. Rev. 99 (3), 1053–1069. ISSN 00028282. URL http://www.jstor.org/stable/25592494. King, C.W., Maxwell, J.P., Donovan, A., 2015. Comparing world economic and net energy metrics, part 2: total economy expenditure perspective. Energies 8 (11). ISSN 1996-1073.12347. URL http://www.mdpi.com/1996-1073/8/11/ 12347. http://dx.doi.org/10.3390/en81112347. Kjärstad, J., Johnsson, F., 2009. Resources and future supply of oil. Energy Policy 37 (2), 441–464. ISSN 0301-4215. URL http://www.sciencedirect.com/science/ article/pii/S0301421508005259. http://dx.doi.org/10.1016/j.enpol.2008.09.056. Krichene, N., 2002. World crude oil and natural gas: a demand and supply model. Energy Econ. 24 (6), 557–576. ISSN 0140-9883. URL http://www. sciencedirect.com/science/article/pii/S0140988302000610. http://dx.doi.org/10. 1016/S0140-9883(02)00061-0. Maggio, G., Cacciola, G., 2009. A variant of the Hubbert curve for world oil production forecasts. Energy Policy 37 (11), 4761–4770. ISSN 0301-4215. URL http://www. sciencedirect.com/science/article/pii/S0301421509004492. http://dx.doi.org/10. 1016/j.enpol.2009.06.053. Murphy, D.J., Hall, C.A.S., 2010. Year in review — EROI or energy return on (energy) invested. Ann. N. Y. Acad. Sci. 1185 (1), 102–118. ISSN 1749-6632. URL http:// dx.doi.org/10.1111/j.1749-6632.2009.05282.x. http://dx.doi.org/10.1111/j.17496632.2009.05282.x. Petroleum, British, 2014, Jun. Statistical Review of World Energy 2014. available at: http://goo.gl/thx3c5 Pindyck, R.S., 1999. The long-run evolutions of energy prices. Energy J. 20 (2), 1–27. URL http://www.iaee.org/en/publications/ejarticle.aspx?id=1308. http://dx.doi. org/10.5547/ISSN0195-6574-EJ-Vol20-No2-1. Rehrl, T., Friedrich, R., 2006. Modelling long-term oil price and extraction with a Hubbert approach: the {LOPEX} model. Energy Policy 34 (15), 2413– 2428. ISSN 0301-4215. URL http://www.sciencedirect.com/science/article/pii/ S0301421505000984. http://dx.doi.org/10.1016/j.enpol.2005.03.021.