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Diesel or compressed natural gas? A real options evaluation of the U.S. natural gas boom on fuel choice for trucking fleets
Energy xxx (2015) 1e7
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Diesel or compressed natural gas? A real options evaluation of the U.S. natural gas boom on fuel choice for trucking fleets Hui Xian a, Berna Karali a, *, Gregory Colson a, Michael E. Wetzstein b a b
Department of Agricultural and Applied Economics, The University of Georgia, Athens, GA 30602-7509, USA Department of Agricultural Economics, Purdue University, West Lafayette, IN 47907, USA
a r t i c l e i n f o
a b s t r a c t
Article history: Received 13 February 2015 Received in revised form 29 May 2015 Accepted 16 June 2015 Available online xxx
Prior to the current U.S. shale gas boom, compressed natural gas played only a minor role as a vehicle fleet fuel despite government policies supporting adoption. With new hydraulic-fracturing methods resulting in a natural gas supply surge, a structural shift has occurred in compressed natural gas price dynamics and its relation with diesel prices. This study analyzes the impact of this shift on the optimal fuel choice by return-to-base trucking fleets considering pre- and post-boom compressed natural gas and diesel price dynamics in a real options analysis framework. Optimal decision thresholds for differing size and vehicle miles traveled indicate that even in the absence of supportive government policies, compressed natural gas is a viable and profitable alternative to diesel under the new market conditions. © 2015 Elsevier Ltd. All rights reserved.
Keywords: Compressed natural gas Diesel Real options analysis Trucking fleet
1. Introduction New hydraulic-fracturing methods resulted in abundant U.S. supply of shale gas, which led to a downward trend in natural gas prices since 2009. Prior to the shale gas boom, CNG (compressed natural gas) played only a minor role as a vehicle fleet fuel despite government policies supporting adoption. However, with decreasing natural gas prices the question then arises: is it now economically feasible to switch from diesel to CNG as a vehicle fleet fuel? We answer this question by estimating an optimal price wedge between the two fuel types, which would trigger CNG adoption in a ROA (real options analysis) framework for return-tobase trucking fleets. Previous studies exploring the economic feasibility of adopting CNG as a replacement fuel for diesel have largely relied on traditional NPV (net present value). For example, employing NPV analysis Cohen [8], Engle [17], and Johnson [24] assessed the viability of operating municipal fleets (buses and waste-collection vehicles) on CNG. While NPV analysis has a long history in economic feasibility studies as a simple tool to assess the trade-off between upfront infrastructure sunk costs and future cost savings, it fails to consider the impact of uncertainty in price dynamics.
* Corresponding author. Tel.: þ1 706 542 0750; fax: þ1 706 542 0739. E-mail address: [email protected] (B. Karali).
Not considering price uncertainty prevents any analysis from answering a fleet operator's key question: will natural gas in the future keep its price advantage? Dixit and Pindyck [11] demonstrate that ROA is a more appropriate method for assessing technology adoption decisions in a stochastic price environment. With uncertainty in energy prices and irreversible sunk costs associated from switching among alternative fuels, our study therefore employs a ROA rather than NPV to determine the optimal conditions under which it is profitable for a fleet operator to adopt CNG fuel as opposed to conventional diesel. A wide array of studies have employed ROA to study optimal energy investment decisions, including construction of ethanol plants [20,30e32], wind power and storage [5,25], adoption of renewable-energy-powered equipment [10,18,26,27,41], conversion of traditional agricultural cropland to energy usage [19,35], and policy uncertainty [28,33,34]. In conjunction with ROA, our analysis explicitly considers the structural market shift caused by advances in hydraulic fracturing on the feasibility of adopting CNG fuel. The time period before and after the shale gas boom are evaluated separately to determine the impact of changes in price dynamics on the optimal adoption decisions. If the price-lowering impact of the shale gas boom lasts shorter than projected, then adopting CNG might not be profitable. In this case, NPV analysis will fail to provide an economically sound investment decision while ROA will provide a more reliable decision.
http://dx.doi.org/10.1016/j.energy.2015.06.080 0360-5442/© 2015 Elsevier Ltd. All rights reserved.
Please cite this article in press as: Xian H, et al., Diesel or compressed natural gas? A real options evaluation of the U.S. natural gas boom on fuel choice for trucking fleets, Energy (2015), http://dx.doi.org/10.1016/j.energy.2015.06.080
2
H. Xian et al. / Energy xxx (2015) 1e7
Overall, our results indicate the recent decline in CNG prices and the new dynamics of CNG and diesel fuel prices have had a marked impact upon the profitability and optimal price thresholds for adopting CNG. Specifically, given recent fuel price levels and their stochastic properties, fleet operators of sufficiently sized fleets should switch from diesel- to CNG-fueled vehicles. However, if the post-boom price dynamics are not sustained and the price pattern reverts to its pre-boom state, the feasibility of switching to a CNGfueled fleet vanishes for small size and low mileage fleet operators.
209), which results in the following differential equation for fuel type I ¼ {D, N}:
1 2 2 I 1 2 2 I s p F þ s p F þ rsI sJ pI pJ FIJI þ mI pI FII þ mJ pJ FJI dF I 2 I I II 2 J J JJ ¼ 0; IsJ
where the subscripts of FI refer to the partial derivatives of FI with respect to the corresponding fuel price. The general solution of FI(pI, pJ) is given by Refs. [2,11]:
h F I pI ; pJ ¼ AI pbI I pJ I ;
2. Methods 2.1. Real options analysis Our real options analysis and corresponding formulas are based on Dixit and Pindyck [11] and Paxson [2]. Consider a private, regional trucking fleet currently operating with either diesel- or CNG-fueled trucks, and evaluating whether to switch to an different fuel (CNG or diesel) if market conditions warrant. Optimal switching conditions from incumbent to an alternative fuel are determined by identifying price thresholds. These thresholds are computed under the maintained hypothesis of maximizing the expected present value of future fleet operating net returns plus all possible switching opportunities. It is assumed there is a constant positive sunk cost, KIJ 0 for I s J, associated with switching from the incumbent fuel I ¼ {D, N} to the alternative fuel J ¼ {D, N}, where D and N denote diesel and CNG, respectively. The fuel price series are modeled as two correlated geometric Brownian motion stochastic processes:
dpI ¼ mI pI dt þ sI pI dzI ;
I ¼ fD; Ng
(1)
where pI is the price of the incumbent fuel type, dpI is the change in the price of fuel type I, dt is the time increment, mI is the rate of change or drift rate, and sI is the volatility. The term dzI is the increment of a Wiener process with properties Eðdz2I Þ ¼ dt and E(dzIdzJ) ¼ rdt, where r is the correlation between the changes of the two fuel prices. In a ROA framework, the value of future returns consists of: 1) the expected present value of all future net returns flowing from operating with the incumbent fuel type; 2) the value of an option to switch to the alternative fuel type. While the first part is a function of only the price of the incumbent fuel, the second part is a function of the prices of both incumbent and alternative fuels. The firm's total value function with incumbent fuel type I, VI, is then
h i V I pI ; pJ ¼ E PV I ðpI Þ þ F I pI ; pJ ;
IsJ
(5)
(6)
where AI, bI, and hI are unknown parameters with AI 0, bI and hI satisfy the elliptical equation
1 1 QI ðbI ; hI Þ ¼ s2I bI ðbI 1Þ þ s2J hI ðhI 1Þ þ rsI sJ bI hI þ mI bI 2 2 þ mJ hI d ¼ 0: (7) I
The option value of switching from fuel I to J, F (pI, pJ), should increase if either the incumbent fuel price pI becomes relatively more expensive or the alternative fuel price, pJ, becomes relatively inexpensive. This implies bI > 0 and hI < 0. Substituting Eqs. (4) and (6) into (2) yields the total value function with the incumbent fuel type I
y cI pI h V I pI ; pJ ¼ þ AI pbI I pJ I d d mI
(8)
where AI 0, bI > 0 and hI < 0.
2.2. Solving for optimal switching thresholds As illustrated in Fig. 1, the theoretical result is two optimal switching boundaries: 1) boundary 1 (p1D, p1N) for switching from diesel to CNG; 2) boundary 2 (p2D, p2N) for switching from CNG to diesel. When fuel prices fall below boundary 1, it is profitable for a firm to use CNG-fueled trucks. When the prices of both fuel types are above boundary 2, then it is optimal for a firm to operate on
(2)
where E½$, PV, and F denote the expectation operator, present value, and option value, respectively. The expected present value of net returns from running on the incumbent fuel fleet is formulated as:
3 Z∞ dt E PV ðpI Þ ¼ E4 ½ y ðcI þ pI Þe dt 5; h
I
i
2
(3)
0
where y is revenue (assumed to be the same for both fuel types), cI is the operating cost of the trucking fleet with fuel type I, and d is the cost of capital. Integrating Eq. (3) yields:
h i yc pI I E PV I ðpI Þ ¼ : d d mI
(4)
For the second term on the right-hand-side of Eq. (2), consider the dynamic-programming approach in Dixit and Pindyck ([11], p.
Fig. 1. Theoretical switching boundaries.
Please cite this article in press as: Xian H, et al., Diesel or compressed natural gas? A real options evaluation of the U.S. natural gas boom on fuel choice for trucking fleets, Energy (2015), http://dx.doi.org/10.1016/j.energy.2015.06.080
H. Xian et al. / Energy xxx (2015) 1e7
diesel-fueled trucks. When fuel prices are between the two boundaries, then the firm should stay with the incumbent fuel type. First consider a fleet operator with a diesel-fueled fleet. If the optimal threshold (p1D, p1N) is reached, then the operator incurs a sunk-switching cost KDN and exercises the switching option. The operator then loses a stream of revenue valued at VD(p1D, p1N) and gains a stream valued at VN(p1D, p1N). The value-matching condition at the optimal threshold (p2D, p2N) is then
V D ðp1D ; p1N Þ ¼ V N ðp1D ; p1N Þ KDN :
(9a)
Similarly, for the operator currently with a CNG-fueled fleet, the value-matching condition at the optimal threshold (p2D, p2N) is
V N ðp2D ; p2N Þ ¼ V D ðp2D ; p2N Þ KND :
(9b)
Substituting Eqs. (8) into (9) yields
y cD pD y cN pN hD hN D N þ AD pb1D p1N ¼ þ AN pb1N p1D d d mD d d mN KDN ; (10a) y cN pN y cN pD hN hD N D þ AN pb2N p2D ¼ þ AD pb2D p2N d d mN d d mD KND : (10b) The smooth-pasting conditions associated with Eq. (10) are D 1 D AD bD pb1D p1N
h
h 1
D D AD hD pb1D p1N
h 1
N N AN hN pb2N p2D
1 hN 1 bN ¼ AN hN p1D p1N ; d mD
(11b)
D 1 D ¼ AD bD pb2D p2N
1 ; d mD
(11c)
1 hD 1 D ¼ AD hD pb2D p2N : d mN
(11d)
N 1 N AN bN pb2N p2D
h
h
dlnpI ¼
1 ; d mN
h
Administration [14,15] and adjusted to January 2013 dollars using the PPI (Producer Price Index) for all commodities, available from the Bureau of Labor Statistics [6]. CNG prices are converted to dollars per DGE by a conversion factor that one thousand cubic feet of CNG equals 7.46 DGE [13]. Fig. 2 illustrates the resulting real price series. Prior to 2007, the two fuel price series exhibited similar patterns, generally moving in the same direction with diesel prices appearing to be slightly more volatile. However, diesel prices generally followed the marked 2007 increase in oil prices during the run up to the 2008 Great Recession, followed by a precipitous decline during the recession. During the subsequent recovery period, the two fuel prices diverged as CNG price declined following the surge in the U.S. supply resulting from new hydraulic fracturing technology. With tight oil markets, diesel prices generally recovered from the recession dip. By employing a Bai-Perron structural change test, break points for CNG and diesel price series are found in July 2009 and October 2009, respectively, at the 1% significance level. Considering the end of the 2008 Great Rrecession in June 2009 and the increase in the shale gas production in 2009 [16], the sample period is split into two subsamples: October 1983eJune 2009 (pre-boom) and July 2009eJune 2014 (post-boom). Analyzing these two sample periods separately allows an investigation of the shale gas boom impact on fleet operators' decisions. Based on the price behaviors illustrated in Fig. 2, it is hypothesized that price thresholds will be relatively more favorable for switching from diesel to CNG in the post-boom period. For testing this hypothesis, the natural logarithm of price series, which follow a simple Brownian motion, are considered as:
(11a)
N 1 N ¼ AN bN pb1N p1D
In addition, the elliptical equations have to satisfy:
QD ðbD ; hD Þ ¼ 0;
(12a)
QN ðbN ; hN Þ ¼ 0:
(12b)
Eqs. (11) and (12) can be solved simultaneously by treating switching price thresholds as pairs rather than individual price levels, thereby reducing the number of unknowns to the number of equations. Imposing the restrictions AD > 0, AN > 0, bD > 0, bN > 0, hD 0, hN 0, p1D > p2D, p1N < p2N, Eqs. (11) and (12) are solved numerically [2].
3
1 mI s2I dt þ sI dzI ≡aI dt þ sI dzI ; 2
I ¼ fD; Ng:
(13)
Both augmented Dickey-Fuller and PhillipsePerron tests fail to reject the existence of a unit root in lnpI, I ¼ {D, N} at the 10% significance level. Thus, the first difference of lnpI is employed for estimating the drift (aI), volatility (sI), and correlation (r) parameters of the ROA. The drift parameter of the original price series is then recovered as mI ¼ aI þ 12s2I . Table 1 presents summary statistics of the first difference of log fuel prices along with the estimated stochastic parameters for three periods: whole and pre- and post-boom periods. The mean prices, over the whole period, are $1.66 and $1.37 for diesel and CNG, respectively. During the post-boom period, mean diesel prices increased and CNG prices declined relative to the whole period. CNG prices are overall less volatile than the diesel prices across all sample periods. The drift rates of CNG prices are also lower relative to diesel prices across all periods. Of particular note is the negative
4.5 4.0
Diesel Price
3.5
CNG Price
1.5 1.0
0.5 Jul-13
Feb-12
Apr-09
Sep-10
Nov-07
Jan-05
Jun-06
Aug-03
Oct-00
Mar-02
May-99
Jul-96
Dec-97
Feb-95
Apr-92
Sep-93
Nov-90
Jan-88
Jun-89
0.0 Aug-86
Monthly spot prices for diesel and CNG were collected from October 1983 to June 2014. Specifically, the series “U.S. No 2 Diesel Retail Sales by Refiners” and “U.S. Price of Natural Gas Sold to Commercial Consumers” are employed for diesel ($/DGE, (diesel gallon equivalent)) and CNG ($/thousand cubic feet), respectively. Both series are obtained from the U.S. Energy Information
2.0
Oct-83
3.1. Fuel prices and stochastic parameters
2.5
Mar-85
3. Data
$/DGE
3.0
Fig. 2. Diesel and CNG prices during October 1983eJune 2014.
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H. Xian et al. / Energy xxx (2015) 1e7
Table 1 Descriptive statistics of fuel prices and stochastic parameters. Period
Whole Pre-boom Post-boom
Mean ($/DGE)
Drift (m)
Standard deviation
Correlation (r)
Volatility (s)
Diesel
CNG
Diesel
CNG
Diesel
CNG
Diesel
CNG
Diesel & CNG
1.66 1.42 2.94
1.37 1.39 1.23
0.83 0.65 0.33
0.28 0.29 0.14
0.042 0.038 0.066
0.002 0.007 0.031
0.212 0.227 0.115
0.116 0.116 0.113
0.035 0.008 0.311
Notes. Whole sample period is from October 1983 to June 2014. Pre- and post-boom refer to sample periods before and after July 2009, respectively. Stochastic parameters are calculated using the first difference of logarithm of prices. DGE denotes diesel gallon equivalent.
Table 2 Sunk costs ($) for a CNG station. Annual VMT
Fleet size Small
Medium
Large
Low Moderate High
437,500 (0.438) 437,500 (0.313) 637,500 (0.319)
675,000 (0.338) 675,000 (0.241) 875,000 (0.219)
912,500 (0.304) 912,500 (0.217) 1,112,500 (0.185)
Notes. Low, moderate, and high VMT (vehicle-miles-traveled) represent 50,000, 70,000, and 100,000 miles, respectively. Small, medium, and large fleet size represents 20, 40, and 60 trucks, respectively. The numbers in parentheses are sunk costs per mile.
CNG price drift in the post-boom period, which is probably indicating the impact of the shale gas boom. The correlation coefficient is significant at the 5% level and negative in the post-boom period, supporting the diverging trend of the two fuel prices.
3.2. Cost data For determining the sunk costs, three different fleet sizes are considered: 20, 40, and 60 trucks representing small, medium, and large fleets, respectively. On average, annual VMT (vehicle-milestraveled) for class 7e8 trucks is approximately 70,000 miles [9]. Considering this mileage as the moderate case, 50,000 and 100,000 miles are deemed as low and high VMT, respectively. Thus, a total of nine different scenarios are considered in the analysis. When a fleet operator makes the decision to switch fuel types a new fueling station is required along with the purchase of new trucks. In terms of CNG, there are two common types of refueling stations: fast- and slow-fill stations. A fast-fill station is more expensive because it requires a high-pressure storage system. The current analysis assumes a fleet operates only during the day and refuels overnight at a slow-fill station. The estimated costs to build a slow-fill station are collected from different sources [4,36,38,40]. Table 2 summarizes the sunk costs associated with building a CNG station. Total sunk costs range from $437,500 to $1,112,500. The highest cost per mile is associated with the low VMT-small fleet case and the lowest cost per mile is observed at the opposite extreme for the high VMT-large fleet case. In contrast, the sunk costs of a conventional gasoline/diesel station range from $50,000 to $150,000 [21]. This is approximately 10% of the CNG station cost. Therefore, for each scenario, the sunk costs of a diesel station are set at 10% of the CNG station sunk costs.1 To simplify the analysis a fleet operator is assumed to rent or finance new trucks at a fixed annual rate. This implies the costs of fleet acquisition are considered as operating cash flows rather than sunk costs. Total operating costs include fleet rental/financing fees, M&O (maintenance and operating) costs for the fleet and station,
1
Only the mechanical system costs related to construction of new fuel stations are compared, with the other related costs, including land expenses, are assumed identical for diesel and CNG stations.
and fuel taxes. Fleet costs are collected from various reports and market data [12,24,37,39,40]. Diesel-fueled fleets, on average, cost $100,000 per truck compared to CNG-fueled trucks at $140,000. Assuming 15 years of useful truck lifespan and a 10% cost of capital, annual financing (rental) costs are computed in dollars per DGE.2 Data for M&O costs are collected from the VICE (Vehicle and Infrastructure Cash-Flow Evaluation) model built by the National Renewable Energy laboratory [24]. Accordingly, average M&O costs for diesel and CNG trucks, including truck rental costs, are $3.364/ DGE and $3.026/DGE, respectively. The M&O cost for a diesel station is $0.198/DGE [29], while the M&O cost for a CNG station is $0.264/DGE [1,39]. For a CNG station, a fleet operator must compress the natural gas onsite, which requires the use of electricity. This compression cost is estimated to be $0.16/DGE [1,39]. The federal excise tax on diesel and CNG is $0.244/DGE and $0.208/ DGE, respectively [3]. Average state taxes are $0.310/DGE for both diesel and CNG [22]. Operating costs for both diesel- and CNGfueled fleets are summarized in Table 3. Notice for a given VMT, it is more expensive to operate a CNG-fueled fleet net of fuel costs. For both diesel and CNG fleets, the high VMT case exhibits the lowest operating cost net of fuel costs as the cost is distributed over a larger number of miles traveled. 4. Empirical results Optimal switching price thresholds are obtained by simultaneously solving Eqs. (11) and (12) for the pre- and post-boom periods separately. Results for all combinations of fleet size and VMT are presented in Fig. 3. The dashed lines in Fig. 3 represent optimal switching boundaries in the pre-boom period, while the solid lines represent those in the post-boom period. The lower two lines characterize boundary 1, which is the optimal threshold of switching from diesel to CNG. The upper two lines characterize boundary 2, which is the optimal threshold of switching from CNG to diesel. If a boundary is below the 45-degree line, it simply means that the CNG trigger price is lower than the diesel price. On the other hand, if a boundary is above the 45-degree line, then the CNG trigger price is higher than the diesel price. Overlaid on the plot are historical price pairs observed in the pre-boom period of 1983e2009 (represented by circles) and post-boom period of 2009e2014 (represented by asterisks). We focus on the results for a medium fleet size with 40 trucks and a moderate VMT of 70,000 miles, which are illustrated in Fig. 4. Boundary 1 in Fig. 4 is below the 45-degree line in both sample periods, implying that the CNG price must be lower than the diesel price for a firm to switch from diesel to CNG. This is due to the higher switching cost from diesel to CNG and higher operating cost of a CNG fleet (Tables 2 and 3). In contrast, boundary 2 is below the
2 Davis et al. [9] report that heavy-duty diesel trucks have fuel efficiency of 6.47 miles/DGE. On the other hand, heavy-duty CNG trucks are estimated to have a 0%e15% lower fuel efficiency than diesel trucks [1,7,23]. In the current study, a 5% lower fuel efficiency is assumed yielding 6.15 miles/DGE.
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H. Xian et al. / Energy xxx (2015) 1e7
5
Table 3 Operating costs ($/DGE) for diesel- and CNG-fueled fleets. Truck cost
Diesel CNG
Maintenance and operating cost
Tax
Low VMT
Moderate VMT
High VMT
Truck
Station
Compression
Federal
State
Low VMT
Total cost Moderate VMT
High VMT
1.717 2.285
1.227 1.632
0.859 1.143
3.364 3.196
0.198 0.264
N/A 0.160
0.244 0.208
0.310 0.310
5.833 6.423
5.343 5.770
4.975 5.281
Notes. Low, moderate, and high VMT (vehicle-miles-traveled) represent 50,000, 70,000, and 100,000 miles, respectively. DGE denotes diesel gallon equivalent.
Fig. 3. Optimal switching boundaries for various fleet size and VMTs.
4.5 Pre-Boom Switching Boundary Post-Boom Switching Boundary Pre-Boom Fuel Prices Post-Boom Fuel Prices
4
Price of CNG ($/DGE)
3.5 3 2.5 2 1.5 1 0.5 0
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
Price of diesel ($/DGE)
Fig. 4. Optimal switching boundaries for medium fleet size and moderate VMT.
45-degree line only at low price levels in both sample periods, implying that even at relatively high diesel prices it is optimal to switch from CNG to diesel. A smaller switching cost from CNG to diesel and a lower operating cost of a diesel-fueled fleet make the switching decision relatively easier at low price levels. As illustrated in Fig. 4, the new price dynamics following the shale gas boom has a greater impact on the optimal boundary for switching from diesel to CNG (boundary 1) than it has on the switching boundary from CNG to diesel (boundary 2). Pre-boom fuel price pairs that lie between the boundary 1 of pre-boom and post-boom periods become optimal in the latter period, thereby increasing the area of the CNG region with a corresponding narrower waiting region. Table 4 presents point estimates of CNG threshold prices for a given diesel price in both pre- and post-boom periods for various fleet size and VMT combinations. These point estimates are selected from the points shown in Figs. 3 and 4. Table 4 should be read such that, when the diesel price is, for instance, $2.00 in the pre-boom
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Table 4 Price thresholds ($/DGE) for switching from diesel- to CNG-fueled fleets. VMT
Pre-boom
Post-boom
Diesel price
Diesel price
2.00
2.50
3.00
3.50
CNG threshold price Small
Medium
Large
Low Moderate High Low Moderate High Low Moderate High
0.40 0.61 0.76 0.41 0.63 0.79 0.42 0.64 0.81
0.74 0.98 1.12 0.77 1.00 1.17 0.77 1.01 1.19
1.10 1.34 1.50 1.13 1.38 1.55 1.14 1.39 1.57
2.00
2.50
3.00
Small FS-Low VMT
3.50
CNG threshold price 1.46 1.72 1.87 1.50 1.76 1.94 1.52 1.78 1.97
0.79 0.97 1.10 0.80 0.98 1.11 0.80 0.98 1.12
1.20 1.39 1.52 1.21 1.39 1.53 1.21 1.40 1.54
1.61 1.80 1.93 1.62 1.81 1.95 1.62 1.81 1.96
2.02 2.22 2.35 2.03 2.23 2.37 2.04 2.23 2.38
Notes. Low, moderate, and high VMT (vehicle-miles-traveled) represent 50,000, 70,000, and 100,000 miles, respectively. Small, medium, and large fleet size represents 20, 40, and 60 trucks, respectively. Pre- and post-boom refer to sample periods before and after July 2009, respectively. DGE denotes diesel gallon equivalent.
period, it is optimal for a small fleet with low VMT to switch to CNG only when the CNG price is $0.40 or lower. For the same level of diesel price, the threshold CNG price for the small fleet with low VMT increases to $0.79 in the post-boom period.
5. Discussion To compare and contrast estimated price thresholds in pre- and post-boom periods we take a closer look at Table 4. Historical price series indicate that the average real price of diesel was $3.10/DGE over the last half of 2008, and $3.00/DGE in the first half of 2014. In the same time periods, the average real price of CNG was $1.90/DGE and $1.20/DGE, respectively. Table 4 lists that at a diesel price of $3.00/DGE, a CNG price lower than $1.38/DGE in the pre-boom period would trigger the switch from diesel- to CNG-fueled vehicles for a fleet of medium size with moderate VMT. Comparing this price threshold to the realized average price of $1.90/DGE, it is observed that the switch to CNG was not economically feasible in the pre-boom period. In the post-boom period, however, the trigger price for CNG is higher at $1.81/DGE. Thus, a CNG price lower than $1.81/DGE is sufficient to make the switch from diesel- to CNGfueled vehicles optimal when the diesel price is $3.00. Comparing this higher trigger price to the average real price of CNG at $1.20/ DGE, it is observed that switching to CNG is feasible in the postboom period. Even with the significant declines in crude oil prices in the second half of 2014, which has resulted in diesel prices falling below $2.80/DGE, natural gas still remains a competitive alternative. Similarly, for any other fleet size and VMT combinations, the optimal CNG threshold price in the post-boom period is higher than its pre-boom counterpart, implying a less rigid price threshold to switch to CNG. Table 4 also indicates that for a given fleet size, increased VMT enhances the profitable adoption of CNG at even higher CNG prices. Higher VMT results in lower sunk costs per mile and therefore yields less restrictive price thresholds. In contrast, increasing VMT does not affect diesel price thresholds markedly (Fig. 3). This result is due to the relatively low sunk costs associated with switching to a diesel-fueled fleet. To further investigate the impact of the shale gas boom we focus on the changes in the estimated CNG price thresholds from pre- to post-boom period. For this purpose, Fig. 5 illustrates the varying impact of the shale gas boom on the CNG adoption feasibility listed in Table 4 for selected fleet size-VMT combinations. Specifically, Fig. 5 shows the magnitude of the differences between the post-
Post-Boom CNG Price - Pre-Boom CNG Price ($/DGE)
Fleet size
0.70 0.60
Medium FS-Moderate VMT Large FS-High VMT
0.50 0.40 0.30 0.20 0.10 0.00 2.0
2.5
3.0
3.5
Price of Diesel ($/DGE)
Fig. 5. Changes in CNG price thresholds after the shale gas boom.
and pre-boom CNG threshold prices at selected diesel prices for variations in fleet size and VMT. For example, from Table 4 it is seen that at a diesel price of $2.00/DGE the CNG threshold prices for a small fleet size with low VMT are $0.40/DGE in the pre-boom and $0.79/DGE in the post-boom period. Thus, Fig. 5 illustrates the $0.39/DGE difference between these two prices on the vertical axis at a diesel price of $2.00/DGE given on the horizontal axis. From Fig. 5, it is clear that for any fleet size, the changes in the CNG threshold prices increase with diesel prices. As diesel fuel becomes relatively more expensive in the post-boom period, the CNG switching boundary becomes less restrictive, yielding higher CNG threshold prices. Further, the shale gas boom has a greater impact on smaller fleets relative to larger fleets. At a diesel price of $2.50/DGE, the CNG threshold price for a small fleet operating low VMT shifts by $0.46/DGE while for a large fleet operating high VMT it shifts by only $0.35/DGE. Overall, the results of the real options analysis indicate that the natural gas boom had a marked impact on the optimal fuel choice decision of fleet operators. Historically, CNG was uncompetitive with diesel as a fleet fuel due to the high sunk costs of fueling stations and trucks. In the absence of government policies to defray these costs, the pre-boom diesel and CNG price levels and dynamics rendered CNG unprofitable for even large and high mileage returnto-base fleets. With the structural shift in the CNG market from the shale gas boom, results indicate that CNG is now a competitive fuel choice for both smaller and larger fleets. Under the post-2009 fuel price dynamics, the optimal CNG switching thresholds have shifted substantially, thus reducing the required wedge between diesel and CNG prices for adoption to be profitable. While the profitability of converting to CNG is greatest for large fleets operating high mileage, this impact of the CNG boom is not uniform across fleet sizes and mileage, with the greatest shift occurring for smaller and low VMT fleets. These results indicate that the natural gas vehicle market will continue to grow due to market conditions, even without government incentives for adoption. 6. Conclusions In this study, the economic feasibility of switching shipping fleets from diesel to compressed natural gas was investigated. Results from the real options analysis provide new evidence that the combined effect of (1) a decline in natural gas prices and (2) a structural shift in the relationship between diesel and natural gas prices has resulted in CNG emerging as a viable fuel choice for intraday shipping fleets. Critically for fleet operators, the economic
Please cite this article in press as: Xian H, et al., Diesel or compressed natural gas? A real options evaluation of the U.S. natural gas boom on fuel choice for trucking fleets, Energy (2015), http://dx.doi.org/10.1016/j.energy.2015.06.080
H. Xian et al. / Energy xxx (2015) 1e7
feasibility of adopting CNG holds even in the absence of government incentives. The results presented can be used by fleet operators to determine, given current market price conditions, the optimal fleet fuel for their operations and guide truck replacement decisions. In order to minimize the costs associated with sub-optimal investments in stochastic price environments an investment decision tool which considers price uncertainty should be used. The real options analysis employed in this study offers a distinct advantage over traditional net present value analysis by incorporating price uncertainty and investment timing. This analysis could be extended to consider related feasibility studies. Our analysis has focused on intra-day shipping fleets, but the methodology is directly applicable to other investment decisions including buses, refuse-trucks, and other return-to-base trucking operations. References [1] Adams R, Horne DB. Compressed natural gas (CNG) transit bus experience survey: April 2009eApril 2010. National Renewable Energy Laboratory; 2010. [2] Adkins R, Paxson D. Reciprocal energy-switching options. J Energy Mark 2011;4:91e120. [3] API. American Petroleum Institute, motor fuel taxes. 2014. available at: http:// www.api.org/oil-and-natural-gas-overview/industry-economics/fuel-taxes. [4] Autogas for America. American, abundant… and affordable? A cost analysis of natural gas vehicles (NGVS) and fueling infrastructure. 2011. available at: http://www.autogasforamerica.org/pdf/Alternative%20Fuel%20Fact%20Brief-% 20Natural%20Gas.pdf. [5] Barroso MM, Iniesta JB. A valuation of wind power projects in Germany using real regulatory options. Energy 2014;77:422e33. [6] BLS. Producer price index. 2014. All Commodities, available at: http://www. bls.gov/ppi/#tables. [7] Chandler K, Walkowitcz K, Clark N. United parcel service (UPS) CNG truck fleet: final results. National Renewable Energy Laboratory; 2002. [8] Cohen JT. Diesel vs. compressed natural gas for school buses: a costeffectiveness evaluation of alternative fuels. Energy Policy 2005;33:1709e22. [9] Davis SC, Diegel SW, Boundy RG. Transportation energy data book. 32 ed. United States: Department of Energy; 2013. [10] Detert N, Kotani K. Real options approach to renewable energy investments in Mongolia. Energy Policy 2013;56:136e50. [11] Dixit AK, Pindyck RS. Investment under uncertainty. United States of America: Princeton University Press; 1994. [12] Dixon O. Is natural gas a viable alternative fuel for HD trucking?. Automotive World Megatrends Magazine; 2013. available at: http://www. automotiveworld.com/megatrends-articles/natural-gas-viable-alternativefuel-hd-trucking/. [13] DOE. Clean cities alternative fuel price report. U.S: Department of Energy; 2014. http://www.afdc.energy.gov/uploads/publication/alternative_fuel_price_ report_january_2014.pdf. [14] EIA. U.S. No 2 diesel retail sales by refiners. Energy Information Administration; 2014. available at: http://www.eia.gov/dnav/pet/hist/LeafHandler.ashx? n¼PET&s¼EMA_EPD2D_PTG_NUS_DPG&f¼M. [15] EIA. U.S. price of natural gas sold to commercial consumers. Energy Information Administration; 2014. available at: http://www.eia.gov/dnav/ng/hist/ n3020us3m.htm. [16] EIA. U.S. natural gas gross withdrawals from shale gas. Energy Information Administration; 2014. available at: http://www.eia.gov/dnav/ng/hist/ngm_ epg0_fgs_nus_mmcfa.htm. [17] Engle D. Fueling with natural gas. MSW Manag 2010;20(2):4e11. [18] Fleten SE, Maribu KM, Wangensteen I. Optimal investment strategies in decentralized renewable power generation under uncertainty. Energy 2007;32:803e15. [19] Gazheli A, Corato LD. Land-use change and solar energy production: a real option approach. Agric Finance Rev 2013;73:507e25. [20] Gonzalez A, Karali B, Wetzstein ME. A public policy aid for bioenergy investment: case study of failed plants. Energy Policy 2012;51:465e73. [21] Idaho National Laboratory. Natural gas technologies low-cost refueling station. 2014. available at: http://www.inl.gov/lng/projects/refuelingstation. shtml. [22] IFTA. International fuel tax association, inc. 2014. 4th Quarter 2014 Fuel Tax Rates, available at: http://www.iftach.org/taxmatrix3/choose_tableq2.php. [23] Jackson M, Schubel R, Kassoy E. Comparative costs of 2010 heavy-duty diesel and natural gas technologies. Natural Gas Vehicle Technology Forum; 2005. [24] Johnson C. Business case for compressed natural gas in municipal fleets. National Renewable Energy Laboratory; 2010. [25] Kroniger D, Madlener R. Hydrogen storage for wind parks: a real options evaluation for optimal investment in more flexibility. Appl Energy 2014;136: 931e46.
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[26] Lee S-C, Shih L-H. Renewable energy policy evaluation using real option model: the case of Taiwan. Energy Econ 2010;32:S67e78. [27] Lima D, Colson G, Karali B, Guerrero B, Amosson S, Wetzstein ME. A new look at the economic evaluation of wind energy as an alternative to electric and natural gas-powered irrigation. J Agric Appl Econ 2013;45:739e51. [28] Linnerud K, Andersson AM, Fleten SE. Investment timing under uncertain renewable energy policy: an empirical study of small hydropower projects. Energy 2014;78:154e64. [29] Lowell D. Clean diesel versus CNG buses: cost, air quality, & climate impacts. Clean Air Task Force; 2012. available at: http://www.catf.us/resources/ publications/view/162. [30] Maxwell C, Davison M. Using real option analysis to quantify ethanol policy impact on the firm's entry into and optimal operation of corn ethanol facilities. Energy Econ 2014;42:140e51. [31] Schmit TM, Luo J, Tauer LW. Ethanol plant investment using net present value and real options analyses. Biomass Bioenergy 2009;33:1442e51. [32] Schmit TM, Luo J, Conrad J. Estimating the influence of US ethanol policy on plant investment decisions: a real options analysis with two stochastic variables. Energy Econ 2011;33:1194e205. [33] Shahnazari M, McHugh A, Maybee B, Whale J. Evaluation of power investment decisions under uncertain carbon policy: a case study for converting coal fired steam turbine to combined cycle gas turbine plants in Australia. Appl Energy 2014;118:241e79. [34] Shahnazari M, McHugh A, Maybee B, Whale J. The effect of political cycles on power investment decisions: expectations over the repeal and reinstatement of carbon policy mechanisms in Australia. Appl Energy 2014;130:157e65. [35] Song F, Zhou J, Swinton SM. Switching to perennial energy crops under uncertainty and costly reversibility. Am J Agric Econ 2011;93:768e83. [36] Stitzer J. Clean cities presentation. 2012. PECO CNG PROGRAM, available at: http://phillycleancities.org/wp-content/uploads/2012/03/peco-ngv.pdf. [37] Tam S. Energy use and policy in the U.S. trucking sector. Act Resaerch Co; 2012. available at: http://www.rff.org/Documents/Events/121010_trucking_ event/Tam-presentation.pdf. [38] TIAX LLL. U.S. and Canadian natural gas vehicle market analysis: compressed natural gas infrastructure. 2012. [39] Werpy MR, Burnham A, Mintz M, Santini DJ. Natural gas vehicles: status, barriers, and opportunities. 2010 (No. ANL/ESD/10-4). [40] Whyatt GA. Issues affecting adoption of natural gas fuel in light-and heavyduty vehicles. Pacific Northwest National Laboratory; 2010. [41] Xian H, Colson G, Mei B, Wetzstein ME. Assessing the feasibility of cofiring wood pellets with coal for electricity generation: a real option analysis. Southern Agricultural Economics Association; 2014. 2014 Annual Meeting.
Nomenclature
Variables and constants AI: unknown parameter in option value function FI: option value KIJ : sunk cost PV I : present value PV I : total value function cI : operating cost dpI : change in the fuel price dt: time increment dzI : the increment of a Wiener process pI : fuel price ðp1D ; p1N Þ: switching boundary from diesel to compressed natural gas ðp2D ; p2N Þ: switching boundary from compressed natural gas to diesel y: revenue Greek symbols aI : drift rate of first differenced log prices bI : unknown parameter in option value function hI : unknown parameter in option value function mI : drift rate of price sI : volatility r: correlation between the changes of the two fuel prices d: cost of capital Subscript D: diesel I: incumbent fuel J: alternative fuel N: compressed natural gas
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Contents lists available at ScienceDirect
Energy journal homepage: www.elsevier.com/locate/energy
Diesel or compressed natural gas? A real options evaluation of the U.S. natural gas boom on fuel choice for trucking fleets Hui Xian a, Berna Karali a, *, Gregory Colson a, Michael E. Wetzstein b a b
Department of Agricultural and Applied Economics, The University of Georgia, Athens, GA 30602-7509, USA Department of Agricultural Economics, Purdue University, West Lafayette, IN 47907, USA
a r t i c l e i n f o
a b s t r a c t
Article history: Received 13 February 2015 Received in revised form 29 May 2015 Accepted 16 June 2015 Available online xxx
Prior to the current U.S. shale gas boom, compressed natural gas played only a minor role as a vehicle fleet fuel despite government policies supporting adoption. With new hydraulic-fracturing methods resulting in a natural gas supply surge, a structural shift has occurred in compressed natural gas price dynamics and its relation with diesel prices. This study analyzes the impact of this shift on the optimal fuel choice by return-to-base trucking fleets considering pre- and post-boom compressed natural gas and diesel price dynamics in a real options analysis framework. Optimal decision thresholds for differing size and vehicle miles traveled indicate that even in the absence of supportive government policies, compressed natural gas is a viable and profitable alternative to diesel under the new market conditions. © 2015 Elsevier Ltd. All rights reserved.
Keywords: Compressed natural gas Diesel Real options analysis Trucking fleet
1. Introduction New hydraulic-fracturing methods resulted in abundant U.S. supply of shale gas, which led to a downward trend in natural gas prices since 2009. Prior to the shale gas boom, CNG (compressed natural gas) played only a minor role as a vehicle fleet fuel despite government policies supporting adoption. However, with decreasing natural gas prices the question then arises: is it now economically feasible to switch from diesel to CNG as a vehicle fleet fuel? We answer this question by estimating an optimal price wedge between the two fuel types, which would trigger CNG adoption in a ROA (real options analysis) framework for return-tobase trucking fleets. Previous studies exploring the economic feasibility of adopting CNG as a replacement fuel for diesel have largely relied on traditional NPV (net present value). For example, employing NPV analysis Cohen [8], Engle [17], and Johnson [24] assessed the viability of operating municipal fleets (buses and waste-collection vehicles) on CNG. While NPV analysis has a long history in economic feasibility studies as a simple tool to assess the trade-off between upfront infrastructure sunk costs and future cost savings, it fails to consider the impact of uncertainty in price dynamics.
* Corresponding author. Tel.: þ1 706 542 0750; fax: þ1 706 542 0739. E-mail address: [email protected] (B. Karali).
Not considering price uncertainty prevents any analysis from answering a fleet operator's key question: will natural gas in the future keep its price advantage? Dixit and Pindyck [11] demonstrate that ROA is a more appropriate method for assessing technology adoption decisions in a stochastic price environment. With uncertainty in energy prices and irreversible sunk costs associated from switching among alternative fuels, our study therefore employs a ROA rather than NPV to determine the optimal conditions under which it is profitable for a fleet operator to adopt CNG fuel as opposed to conventional diesel. A wide array of studies have employed ROA to study optimal energy investment decisions, including construction of ethanol plants [20,30e32], wind power and storage [5,25], adoption of renewable-energy-powered equipment [10,18,26,27,41], conversion of traditional agricultural cropland to energy usage [19,35], and policy uncertainty [28,33,34]. In conjunction with ROA, our analysis explicitly considers the structural market shift caused by advances in hydraulic fracturing on the feasibility of adopting CNG fuel. The time period before and after the shale gas boom are evaluated separately to determine the impact of changes in price dynamics on the optimal adoption decisions. If the price-lowering impact of the shale gas boom lasts shorter than projected, then adopting CNG might not be profitable. In this case, NPV analysis will fail to provide an economically sound investment decision while ROA will provide a more reliable decision.
http://dx.doi.org/10.1016/j.energy.2015.06.080 0360-5442/© 2015 Elsevier Ltd. All rights reserved.
Please cite this article in press as: Xian H, et al., Diesel or compressed natural gas? A real options evaluation of the U.S. natural gas boom on fuel choice for trucking fleets, Energy (2015), http://dx.doi.org/10.1016/j.energy.2015.06.080
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H. Xian et al. / Energy xxx (2015) 1e7
Overall, our results indicate the recent decline in CNG prices and the new dynamics of CNG and diesel fuel prices have had a marked impact upon the profitability and optimal price thresholds for adopting CNG. Specifically, given recent fuel price levels and their stochastic properties, fleet operators of sufficiently sized fleets should switch from diesel- to CNG-fueled vehicles. However, if the post-boom price dynamics are not sustained and the price pattern reverts to its pre-boom state, the feasibility of switching to a CNGfueled fleet vanishes for small size and low mileage fleet operators.
209), which results in the following differential equation for fuel type I ¼ {D, N}:
1 2 2 I 1 2 2 I s p F þ s p F þ rsI sJ pI pJ FIJI þ mI pI FII þ mJ pJ FJI dF I 2 I I II 2 J J JJ ¼ 0; IsJ
where the subscripts of FI refer to the partial derivatives of FI with respect to the corresponding fuel price. The general solution of FI(pI, pJ) is given by Refs. [2,11]:
h F I pI ; pJ ¼ AI pbI I pJ I ;
2. Methods 2.1. Real options analysis Our real options analysis and corresponding formulas are based on Dixit and Pindyck [11] and Paxson [2]. Consider a private, regional trucking fleet currently operating with either diesel- or CNG-fueled trucks, and evaluating whether to switch to an different fuel (CNG or diesel) if market conditions warrant. Optimal switching conditions from incumbent to an alternative fuel are determined by identifying price thresholds. These thresholds are computed under the maintained hypothesis of maximizing the expected present value of future fleet operating net returns plus all possible switching opportunities. It is assumed there is a constant positive sunk cost, KIJ 0 for I s J, associated with switching from the incumbent fuel I ¼ {D, N} to the alternative fuel J ¼ {D, N}, where D and N denote diesel and CNG, respectively. The fuel price series are modeled as two correlated geometric Brownian motion stochastic processes:
dpI ¼ mI pI dt þ sI pI dzI ;
I ¼ fD; Ng
(1)
where pI is the price of the incumbent fuel type, dpI is the change in the price of fuel type I, dt is the time increment, mI is the rate of change or drift rate, and sI is the volatility. The term dzI is the increment of a Wiener process with properties Eðdz2I Þ ¼ dt and E(dzIdzJ) ¼ rdt, where r is the correlation between the changes of the two fuel prices. In a ROA framework, the value of future returns consists of: 1) the expected present value of all future net returns flowing from operating with the incumbent fuel type; 2) the value of an option to switch to the alternative fuel type. While the first part is a function of only the price of the incumbent fuel, the second part is a function of the prices of both incumbent and alternative fuels. The firm's total value function with incumbent fuel type I, VI, is then
h i V I pI ; pJ ¼ E PV I ðpI Þ þ F I pI ; pJ ;
IsJ
(5)
(6)
where AI, bI, and hI are unknown parameters with AI 0, bI and hI satisfy the elliptical equation
1 1 QI ðbI ; hI Þ ¼ s2I bI ðbI 1Þ þ s2J hI ðhI 1Þ þ rsI sJ bI hI þ mI bI 2 2 þ mJ hI d ¼ 0: (7) I
The option value of switching from fuel I to J, F (pI, pJ), should increase if either the incumbent fuel price pI becomes relatively more expensive or the alternative fuel price, pJ, becomes relatively inexpensive. This implies bI > 0 and hI < 0. Substituting Eqs. (4) and (6) into (2) yields the total value function with the incumbent fuel type I
y cI pI h V I pI ; pJ ¼ þ AI pbI I pJ I d d mI
(8)
where AI 0, bI > 0 and hI < 0.
2.2. Solving for optimal switching thresholds As illustrated in Fig. 1, the theoretical result is two optimal switching boundaries: 1) boundary 1 (p1D, p1N) for switching from diesel to CNG; 2) boundary 2 (p2D, p2N) for switching from CNG to diesel. When fuel prices fall below boundary 1, it is profitable for a firm to use CNG-fueled trucks. When the prices of both fuel types are above boundary 2, then it is optimal for a firm to operate on
(2)
where E½$, PV, and F denote the expectation operator, present value, and option value, respectively. The expected present value of net returns from running on the incumbent fuel fleet is formulated as:
3 Z∞ dt E PV ðpI Þ ¼ E4 ½ y ðcI þ pI Þe dt 5; h
I
i
2
(3)
0
where y is revenue (assumed to be the same for both fuel types), cI is the operating cost of the trucking fleet with fuel type I, and d is the cost of capital. Integrating Eq. (3) yields:
h i yc pI I E PV I ðpI Þ ¼ : d d mI
(4)
For the second term on the right-hand-side of Eq. (2), consider the dynamic-programming approach in Dixit and Pindyck ([11], p.
Fig. 1. Theoretical switching boundaries.
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H. Xian et al. / Energy xxx (2015) 1e7
diesel-fueled trucks. When fuel prices are between the two boundaries, then the firm should stay with the incumbent fuel type. First consider a fleet operator with a diesel-fueled fleet. If the optimal threshold (p1D, p1N) is reached, then the operator incurs a sunk-switching cost KDN and exercises the switching option. The operator then loses a stream of revenue valued at VD(p1D, p1N) and gains a stream valued at VN(p1D, p1N). The value-matching condition at the optimal threshold (p2D, p2N) is then
V D ðp1D ; p1N Þ ¼ V N ðp1D ; p1N Þ KDN :
(9a)
Similarly, for the operator currently with a CNG-fueled fleet, the value-matching condition at the optimal threshold (p2D, p2N) is
V N ðp2D ; p2N Þ ¼ V D ðp2D ; p2N Þ KND :
(9b)
Substituting Eqs. (8) into (9) yields
y cD pD y cN pN hD hN D N þ AD pb1D p1N ¼ þ AN pb1N p1D d d mD d d mN KDN ; (10a) y cN pN y cN pD hN hD N D þ AN pb2N p2D ¼ þ AD pb2D p2N d d mN d d mD KND : (10b) The smooth-pasting conditions associated with Eq. (10) are D 1 D AD bD pb1D p1N
h
h 1
D D AD hD pb1D p1N
h 1
N N AN hN pb2N p2D
1 hN 1 bN ¼ AN hN p1D p1N ; d mD
(11b)
D 1 D ¼ AD bD pb2D p2N
1 ; d mD
(11c)
1 hD 1 D ¼ AD hD pb2D p2N : d mN
(11d)
N 1 N AN bN pb2N p2D
h
h
dlnpI ¼
1 ; d mN
h
Administration [14,15] and adjusted to January 2013 dollars using the PPI (Producer Price Index) for all commodities, available from the Bureau of Labor Statistics [6]. CNG prices are converted to dollars per DGE by a conversion factor that one thousand cubic feet of CNG equals 7.46 DGE [13]. Fig. 2 illustrates the resulting real price series. Prior to 2007, the two fuel price series exhibited similar patterns, generally moving in the same direction with diesel prices appearing to be slightly more volatile. However, diesel prices generally followed the marked 2007 increase in oil prices during the run up to the 2008 Great Recession, followed by a precipitous decline during the recession. During the subsequent recovery period, the two fuel prices diverged as CNG price declined following the surge in the U.S. supply resulting from new hydraulic fracturing technology. With tight oil markets, diesel prices generally recovered from the recession dip. By employing a Bai-Perron structural change test, break points for CNG and diesel price series are found in July 2009 and October 2009, respectively, at the 1% significance level. Considering the end of the 2008 Great Rrecession in June 2009 and the increase in the shale gas production in 2009 [16], the sample period is split into two subsamples: October 1983eJune 2009 (pre-boom) and July 2009eJune 2014 (post-boom). Analyzing these two sample periods separately allows an investigation of the shale gas boom impact on fleet operators' decisions. Based on the price behaviors illustrated in Fig. 2, it is hypothesized that price thresholds will be relatively more favorable for switching from diesel to CNG in the post-boom period. For testing this hypothesis, the natural logarithm of price series, which follow a simple Brownian motion, are considered as:
(11a)
N 1 N ¼ AN bN pb1N p1D
In addition, the elliptical equations have to satisfy:
QD ðbD ; hD Þ ¼ 0;
(12a)
QN ðbN ; hN Þ ¼ 0:
(12b)
Eqs. (11) and (12) can be solved simultaneously by treating switching price thresholds as pairs rather than individual price levels, thereby reducing the number of unknowns to the number of equations. Imposing the restrictions AD > 0, AN > 0, bD > 0, bN > 0, hD 0, hN 0, p1D > p2D, p1N < p2N, Eqs. (11) and (12) are solved numerically [2].
3
1 mI s2I dt þ sI dzI ≡aI dt þ sI dzI ; 2
I ¼ fD; Ng:
(13)
Both augmented Dickey-Fuller and PhillipsePerron tests fail to reject the existence of a unit root in lnpI, I ¼ {D, N} at the 10% significance level. Thus, the first difference of lnpI is employed for estimating the drift (aI), volatility (sI), and correlation (r) parameters of the ROA. The drift parameter of the original price series is then recovered as mI ¼ aI þ 12s2I . Table 1 presents summary statistics of the first difference of log fuel prices along with the estimated stochastic parameters for three periods: whole and pre- and post-boom periods. The mean prices, over the whole period, are $1.66 and $1.37 for diesel and CNG, respectively. During the post-boom period, mean diesel prices increased and CNG prices declined relative to the whole period. CNG prices are overall less volatile than the diesel prices across all sample periods. The drift rates of CNG prices are also lower relative to diesel prices across all periods. Of particular note is the negative
4.5 4.0
Diesel Price
3.5
CNG Price
1.5 1.0
0.5 Jul-13
Feb-12
Apr-09
Sep-10
Nov-07
Jan-05
Jun-06
Aug-03
Oct-00
Mar-02
May-99
Jul-96
Dec-97
Feb-95
Apr-92
Sep-93
Nov-90
Jan-88
Jun-89
0.0 Aug-86
Monthly spot prices for diesel and CNG were collected from October 1983 to June 2014. Specifically, the series “U.S. No 2 Diesel Retail Sales by Refiners” and “U.S. Price of Natural Gas Sold to Commercial Consumers” are employed for diesel ($/DGE, (diesel gallon equivalent)) and CNG ($/thousand cubic feet), respectively. Both series are obtained from the U.S. Energy Information
2.0
Oct-83
3.1. Fuel prices and stochastic parameters
2.5
Mar-85
3. Data
$/DGE
3.0
Fig. 2. Diesel and CNG prices during October 1983eJune 2014.
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H. Xian et al. / Energy xxx (2015) 1e7
Table 1 Descriptive statistics of fuel prices and stochastic parameters. Period
Whole Pre-boom Post-boom
Mean ($/DGE)
Drift (m)
Standard deviation
Correlation (r)
Volatility (s)
Diesel
CNG
Diesel
CNG
Diesel
CNG
Diesel
CNG
Diesel & CNG
1.66 1.42 2.94
1.37 1.39 1.23
0.83 0.65 0.33
0.28 0.29 0.14
0.042 0.038 0.066
0.002 0.007 0.031
0.212 0.227 0.115
0.116 0.116 0.113
0.035 0.008 0.311
Notes. Whole sample period is from October 1983 to June 2014. Pre- and post-boom refer to sample periods before and after July 2009, respectively. Stochastic parameters are calculated using the first difference of logarithm of prices. DGE denotes diesel gallon equivalent.
Table 2 Sunk costs ($) for a CNG station. Annual VMT
Fleet size Small
Medium
Large
Low Moderate High
437,500 (0.438) 437,500 (0.313) 637,500 (0.319)
675,000 (0.338) 675,000 (0.241) 875,000 (0.219)
912,500 (0.304) 912,500 (0.217) 1,112,500 (0.185)
Notes. Low, moderate, and high VMT (vehicle-miles-traveled) represent 50,000, 70,000, and 100,000 miles, respectively. Small, medium, and large fleet size represents 20, 40, and 60 trucks, respectively. The numbers in parentheses are sunk costs per mile.
CNG price drift in the post-boom period, which is probably indicating the impact of the shale gas boom. The correlation coefficient is significant at the 5% level and negative in the post-boom period, supporting the diverging trend of the two fuel prices.
3.2. Cost data For determining the sunk costs, three different fleet sizes are considered: 20, 40, and 60 trucks representing small, medium, and large fleets, respectively. On average, annual VMT (vehicle-milestraveled) for class 7e8 trucks is approximately 70,000 miles [9]. Considering this mileage as the moderate case, 50,000 and 100,000 miles are deemed as low and high VMT, respectively. Thus, a total of nine different scenarios are considered in the analysis. When a fleet operator makes the decision to switch fuel types a new fueling station is required along with the purchase of new trucks. In terms of CNG, there are two common types of refueling stations: fast- and slow-fill stations. A fast-fill station is more expensive because it requires a high-pressure storage system. The current analysis assumes a fleet operates only during the day and refuels overnight at a slow-fill station. The estimated costs to build a slow-fill station are collected from different sources [4,36,38,40]. Table 2 summarizes the sunk costs associated with building a CNG station. Total sunk costs range from $437,500 to $1,112,500. The highest cost per mile is associated with the low VMT-small fleet case and the lowest cost per mile is observed at the opposite extreme for the high VMT-large fleet case. In contrast, the sunk costs of a conventional gasoline/diesel station range from $50,000 to $150,000 [21]. This is approximately 10% of the CNG station cost. Therefore, for each scenario, the sunk costs of a diesel station are set at 10% of the CNG station sunk costs.1 To simplify the analysis a fleet operator is assumed to rent or finance new trucks at a fixed annual rate. This implies the costs of fleet acquisition are considered as operating cash flows rather than sunk costs. Total operating costs include fleet rental/financing fees, M&O (maintenance and operating) costs for the fleet and station,
1
Only the mechanical system costs related to construction of new fuel stations are compared, with the other related costs, including land expenses, are assumed identical for diesel and CNG stations.
and fuel taxes. Fleet costs are collected from various reports and market data [12,24,37,39,40]. Diesel-fueled fleets, on average, cost $100,000 per truck compared to CNG-fueled trucks at $140,000. Assuming 15 years of useful truck lifespan and a 10% cost of capital, annual financing (rental) costs are computed in dollars per DGE.2 Data for M&O costs are collected from the VICE (Vehicle and Infrastructure Cash-Flow Evaluation) model built by the National Renewable Energy laboratory [24]. Accordingly, average M&O costs for diesel and CNG trucks, including truck rental costs, are $3.364/ DGE and $3.026/DGE, respectively. The M&O cost for a diesel station is $0.198/DGE [29], while the M&O cost for a CNG station is $0.264/DGE [1,39]. For a CNG station, a fleet operator must compress the natural gas onsite, which requires the use of electricity. This compression cost is estimated to be $0.16/DGE [1,39]. The federal excise tax on diesel and CNG is $0.244/DGE and $0.208/ DGE, respectively [3]. Average state taxes are $0.310/DGE for both diesel and CNG [22]. Operating costs for both diesel- and CNGfueled fleets are summarized in Table 3. Notice for a given VMT, it is more expensive to operate a CNG-fueled fleet net of fuel costs. For both diesel and CNG fleets, the high VMT case exhibits the lowest operating cost net of fuel costs as the cost is distributed over a larger number of miles traveled. 4. Empirical results Optimal switching price thresholds are obtained by simultaneously solving Eqs. (11) and (12) for the pre- and post-boom periods separately. Results for all combinations of fleet size and VMT are presented in Fig. 3. The dashed lines in Fig. 3 represent optimal switching boundaries in the pre-boom period, while the solid lines represent those in the post-boom period. The lower two lines characterize boundary 1, which is the optimal threshold of switching from diesel to CNG. The upper two lines characterize boundary 2, which is the optimal threshold of switching from CNG to diesel. If a boundary is below the 45-degree line, it simply means that the CNG trigger price is lower than the diesel price. On the other hand, if a boundary is above the 45-degree line, then the CNG trigger price is higher than the diesel price. Overlaid on the plot are historical price pairs observed in the pre-boom period of 1983e2009 (represented by circles) and post-boom period of 2009e2014 (represented by asterisks). We focus on the results for a medium fleet size with 40 trucks and a moderate VMT of 70,000 miles, which are illustrated in Fig. 4. Boundary 1 in Fig. 4 is below the 45-degree line in both sample periods, implying that the CNG price must be lower than the diesel price for a firm to switch from diesel to CNG. This is due to the higher switching cost from diesel to CNG and higher operating cost of a CNG fleet (Tables 2 and 3). In contrast, boundary 2 is below the
2 Davis et al. [9] report that heavy-duty diesel trucks have fuel efficiency of 6.47 miles/DGE. On the other hand, heavy-duty CNG trucks are estimated to have a 0%e15% lower fuel efficiency than diesel trucks [1,7,23]. In the current study, a 5% lower fuel efficiency is assumed yielding 6.15 miles/DGE.
Please cite this article in press as: Xian H, et al., Diesel or compressed natural gas? A real options evaluation of the U.S. natural gas boom on fuel choice for trucking fleets, Energy (2015), http://dx.doi.org/10.1016/j.energy.2015.06.080
H. Xian et al. / Energy xxx (2015) 1e7
5
Table 3 Operating costs ($/DGE) for diesel- and CNG-fueled fleets. Truck cost
Diesel CNG
Maintenance and operating cost
Tax
Low VMT
Moderate VMT
High VMT
Truck
Station
Compression
Federal
State
Low VMT
Total cost Moderate VMT
High VMT
1.717 2.285
1.227 1.632
0.859 1.143
3.364 3.196
0.198 0.264
N/A 0.160
0.244 0.208
0.310 0.310
5.833 6.423
5.343 5.770
4.975 5.281
Notes. Low, moderate, and high VMT (vehicle-miles-traveled) represent 50,000, 70,000, and 100,000 miles, respectively. DGE denotes diesel gallon equivalent.
Fig. 3. Optimal switching boundaries for various fleet size and VMTs.
4.5 Pre-Boom Switching Boundary Post-Boom Switching Boundary Pre-Boom Fuel Prices Post-Boom Fuel Prices
4
Price of CNG ($/DGE)
3.5 3 2.5 2 1.5 1 0.5 0
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
Price of diesel ($/DGE)
Fig. 4. Optimal switching boundaries for medium fleet size and moderate VMT.
45-degree line only at low price levels in both sample periods, implying that even at relatively high diesel prices it is optimal to switch from CNG to diesel. A smaller switching cost from CNG to diesel and a lower operating cost of a diesel-fueled fleet make the switching decision relatively easier at low price levels. As illustrated in Fig. 4, the new price dynamics following the shale gas boom has a greater impact on the optimal boundary for switching from diesel to CNG (boundary 1) than it has on the switching boundary from CNG to diesel (boundary 2). Pre-boom fuel price pairs that lie between the boundary 1 of pre-boom and post-boom periods become optimal in the latter period, thereby increasing the area of the CNG region with a corresponding narrower waiting region. Table 4 presents point estimates of CNG threshold prices for a given diesel price in both pre- and post-boom periods for various fleet size and VMT combinations. These point estimates are selected from the points shown in Figs. 3 and 4. Table 4 should be read such that, when the diesel price is, for instance, $2.00 in the pre-boom
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H. Xian et al. / Energy xxx (2015) 1e7
Table 4 Price thresholds ($/DGE) for switching from diesel- to CNG-fueled fleets. VMT
Pre-boom
Post-boom
Diesel price
Diesel price
2.00
2.50
3.00
3.50
CNG threshold price Small
Medium
Large
Low Moderate High Low Moderate High Low Moderate High
0.40 0.61 0.76 0.41 0.63 0.79 0.42 0.64 0.81
0.74 0.98 1.12 0.77 1.00 1.17 0.77 1.01 1.19
1.10 1.34 1.50 1.13 1.38 1.55 1.14 1.39 1.57
2.00
2.50
3.00
Small FS-Low VMT
3.50
CNG threshold price 1.46 1.72 1.87 1.50 1.76 1.94 1.52 1.78 1.97
0.79 0.97 1.10 0.80 0.98 1.11 0.80 0.98 1.12
1.20 1.39 1.52 1.21 1.39 1.53 1.21 1.40 1.54
1.61 1.80 1.93 1.62 1.81 1.95 1.62 1.81 1.96
2.02 2.22 2.35 2.03 2.23 2.37 2.04 2.23 2.38
Notes. Low, moderate, and high VMT (vehicle-miles-traveled) represent 50,000, 70,000, and 100,000 miles, respectively. Small, medium, and large fleet size represents 20, 40, and 60 trucks, respectively. Pre- and post-boom refer to sample periods before and after July 2009, respectively. DGE denotes diesel gallon equivalent.
period, it is optimal for a small fleet with low VMT to switch to CNG only when the CNG price is $0.40 or lower. For the same level of diesel price, the threshold CNG price for the small fleet with low VMT increases to $0.79 in the post-boom period.
5. Discussion To compare and contrast estimated price thresholds in pre- and post-boom periods we take a closer look at Table 4. Historical price series indicate that the average real price of diesel was $3.10/DGE over the last half of 2008, and $3.00/DGE in the first half of 2014. In the same time periods, the average real price of CNG was $1.90/DGE and $1.20/DGE, respectively. Table 4 lists that at a diesel price of $3.00/DGE, a CNG price lower than $1.38/DGE in the pre-boom period would trigger the switch from diesel- to CNG-fueled vehicles for a fleet of medium size with moderate VMT. Comparing this price threshold to the realized average price of $1.90/DGE, it is observed that the switch to CNG was not economically feasible in the pre-boom period. In the post-boom period, however, the trigger price for CNG is higher at $1.81/DGE. Thus, a CNG price lower than $1.81/DGE is sufficient to make the switch from diesel- to CNGfueled vehicles optimal when the diesel price is $3.00. Comparing this higher trigger price to the average real price of CNG at $1.20/ DGE, it is observed that switching to CNG is feasible in the postboom period. Even with the significant declines in crude oil prices in the second half of 2014, which has resulted in diesel prices falling below $2.80/DGE, natural gas still remains a competitive alternative. Similarly, for any other fleet size and VMT combinations, the optimal CNG threshold price in the post-boom period is higher than its pre-boom counterpart, implying a less rigid price threshold to switch to CNG. Table 4 also indicates that for a given fleet size, increased VMT enhances the profitable adoption of CNG at even higher CNG prices. Higher VMT results in lower sunk costs per mile and therefore yields less restrictive price thresholds. In contrast, increasing VMT does not affect diesel price thresholds markedly (Fig. 3). This result is due to the relatively low sunk costs associated with switching to a diesel-fueled fleet. To further investigate the impact of the shale gas boom we focus on the changes in the estimated CNG price thresholds from pre- to post-boom period. For this purpose, Fig. 5 illustrates the varying impact of the shale gas boom on the CNG adoption feasibility listed in Table 4 for selected fleet size-VMT combinations. Specifically, Fig. 5 shows the magnitude of the differences between the post-
Post-Boom CNG Price - Pre-Boom CNG Price ($/DGE)
Fleet size
0.70 0.60
Medium FS-Moderate VMT Large FS-High VMT
0.50 0.40 0.30 0.20 0.10 0.00 2.0
2.5
3.0
3.5
Price of Diesel ($/DGE)
Fig. 5. Changes in CNG price thresholds after the shale gas boom.
and pre-boom CNG threshold prices at selected diesel prices for variations in fleet size and VMT. For example, from Table 4 it is seen that at a diesel price of $2.00/DGE the CNG threshold prices for a small fleet size with low VMT are $0.40/DGE in the pre-boom and $0.79/DGE in the post-boom period. Thus, Fig. 5 illustrates the $0.39/DGE difference between these two prices on the vertical axis at a diesel price of $2.00/DGE given on the horizontal axis. From Fig. 5, it is clear that for any fleet size, the changes in the CNG threshold prices increase with diesel prices. As diesel fuel becomes relatively more expensive in the post-boom period, the CNG switching boundary becomes less restrictive, yielding higher CNG threshold prices. Further, the shale gas boom has a greater impact on smaller fleets relative to larger fleets. At a diesel price of $2.50/DGE, the CNG threshold price for a small fleet operating low VMT shifts by $0.46/DGE while for a large fleet operating high VMT it shifts by only $0.35/DGE. Overall, the results of the real options analysis indicate that the natural gas boom had a marked impact on the optimal fuel choice decision of fleet operators. Historically, CNG was uncompetitive with diesel as a fleet fuel due to the high sunk costs of fueling stations and trucks. In the absence of government policies to defray these costs, the pre-boom diesel and CNG price levels and dynamics rendered CNG unprofitable for even large and high mileage returnto-base fleets. With the structural shift in the CNG market from the shale gas boom, results indicate that CNG is now a competitive fuel choice for both smaller and larger fleets. Under the post-2009 fuel price dynamics, the optimal CNG switching thresholds have shifted substantially, thus reducing the required wedge between diesel and CNG prices for adoption to be profitable. While the profitability of converting to CNG is greatest for large fleets operating high mileage, this impact of the CNG boom is not uniform across fleet sizes and mileage, with the greatest shift occurring for smaller and low VMT fleets. These results indicate that the natural gas vehicle market will continue to grow due to market conditions, even without government incentives for adoption. 6. Conclusions In this study, the economic feasibility of switching shipping fleets from diesel to compressed natural gas was investigated. Results from the real options analysis provide new evidence that the combined effect of (1) a decline in natural gas prices and (2) a structural shift in the relationship between diesel and natural gas prices has resulted in CNG emerging as a viable fuel choice for intraday shipping fleets. Critically for fleet operators, the economic
Please cite this article in press as: Xian H, et al., Diesel or compressed natural gas? A real options evaluation of the U.S. natural gas boom on fuel choice for trucking fleets, Energy (2015), http://dx.doi.org/10.1016/j.energy.2015.06.080
H. Xian et al. / Energy xxx (2015) 1e7
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Nomenclature
Variables and constants AI: unknown parameter in option value function FI: option value KIJ : sunk cost PV I : present value PV I : total value function cI : operating cost dpI : change in the fuel price dt: time increment dzI : the increment of a Wiener process pI : fuel price ðp1D ; p1N Þ: switching boundary from diesel to compressed natural gas ðp2D ; p2N Þ: switching boundary from compressed natural gas to diesel y: revenue Greek symbols aI : drift rate of first differenced log prices bI : unknown parameter in option value function hI : unknown parameter in option value function mI : drift rate of price sI : volatility r: correlation between the changes of the two fuel prices d: cost of capital Subscript D: diesel I: incumbent fuel J: alternative fuel N: compressed natural gas
Please cite this article in press as: Xian H, et al., Diesel or compressed natural gas? A real options evaluation of the U.S. natural gas boom on fuel choice for trucking fleets, Energy (2015), http://dx.doi.org/10.1016/j.energy.2015.06.080