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Renewable Energy and Transport Fuel Use Patterns Essay

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Updated: Mar 7th, 2022

Introduction

 U.S. Primary Energy Consumption by Source and Sector, 2007 (Quadrillion Btu) Source: EIA “Annual Energy Review”
Figure 1: U.S. Primary Energy Consumption by Source and Sector, 2007 (Quadrillion Btu) Source: EIA “Annual Energy Review”

As policy-making and social consensus unite in favor of stopping climate change in its tracks, it seems vital to assess the degree of change in use of primary energy sources that has actually taken place. The latest available annual review (see Figure 1 alongside) reveals that the challenges remain formidable. The largest “consumer” of primary energy is the power generation industry but “clean” nuclear power and renewable energy together contribute less than a third of energy inputs. No less than nine in ten BTU or “therm” equivalents provided by coal also goes to the power sector.

The transportation sector ranks second in total energy use and is very often the focus of media attention because of the American love affair with cars. The publicity about hydrogen, solar and electric hybrids aside, petroleum still accounted for 96% of energy use at least as of the end of 2007.

Data Analysis

The base data is as follows:

Table 1

Energy Information Administration
April 2009 Monthly Energy Review
Posted: April 29, 2009
Table 2.5. Transportation Sector Energy Consumption
Year Natural Gas Consumed by the Transportation Sector Petroleum Consumed by the Transportation Sector (Excluding Ethanol) Biomass Energy Consumed by the Transportation Sector Electricity Retail Sales to the Transportation Sector
(Trillion Btu) (Trillion Btu) (Trillion Btu) (Trillion Btu)
1973 742.741 17830.704 Not Available 10.532
1974 684.843 17399.279 Not Available 9.721
1975 594.622 17613.972 Not Available 10.149
1976 558.741 18506.132 Not Available 10.059
1977 542.79 19241.152 Not Available 10.429
1978 538.938 20041.477 Not Available 10.027
1979 611.781 19824.588 Not Available 10.117
1980 649.853 19008.5 Not Available 11.069
1981 658.383 18810.956 6.861 10.871
1982 611.918 18420.004 18.658 11.002
1983 505.233 18592.81 34.408 12.675
1984 544.617 19020.074 42.108 14.293
1985 519.383 19470.552 50.753 14.148
1986 499.107 20181.986 58.61 15.057
1987 535.264 20816.441 67.42 15.567
1988 631.715 21566.645 68.495 15.93
1989 648.817 21706.301 69.48 16.276
1990 679.889 21624.643 61.654 16.211
1991 620.324 21373.185 71.525 16.236
1992 608.106 21673.83 81.373 16.056
1993 644.729 21976.147 95.572 16.278
1994 708.538 22496.289 106.979 17.04
1995 723.952 22954.41 114.785 16.973
1996 736.886 23564.714 82.307 16.797
1997 780.312 23812.786 104.047 16.744
1998 666.097 24421.925 115.146 16.929
1999 675.335 25098.192 120.199 17.491
2000 671.991 25681.869 137.64 18.363
2001 658.046 25412.52 144.999 19.531
2002 701.644 25913.023 173.071 18.825
2003 629.885 26063.293 234.468 23.235
2004 602.542 26922.078 295.496 24.647
2005 624.51 27309.485 345.698 25.612
2006 624.975 27652.269 483.965 25.104
2007 666.699 27765.624 613.853 27.885
2008 677.193 26331.99 832.95 26.108

The first segment of this analysis tests for differences between consumption of natural gas and ethanol. Owing to the different power trains required by natural gas and ethanol, we assume that the populations of users are distinct. One way to assess whether patterns of natural gas and ethanol use are similar or distinct is to employ a two-sample t test. Since we further posit that the differences can go either way, we employ the two-tailed test. The formal procedure involves formulating hypotheses to be tested as follows:

H0, the null hypothesis = There is no difference in the population means for both ethanol (1) and natural gas (2). In technical terms, 1 2 = 0.

Ha, the alternative hypothesis = There is a difference between the population means of ethanol and natural gas. In technical terms, H1: 1 2 ≠ 0.

Natural gas has enjoyed a longer tradition of use, starting in this data series (year 1=1973) with around 4.2% of the energy contribution of the motor fuels gasoline, diesel, jet fuel, etc. On the other hand, there was little measurable use for ethanol prior to 1981, when biomass accounted for 6.9 trillion BTU’s of transportation sector use. This factor alone explains why the means for the two fuels are so far apart (averaging 633 trillion and 165 trillion BTU’s, respectively, for NG and biomass, see Table 2 and Figure 2 overleaf). At 467.3, the mean difference between the two fuels lies within the bounds of the 95% confidence interval of the difference (390 at the lower bound and 545 at the upper bound). The magnitude of these results is confirmed by the finding that the significance value for the two-tailed test, s < 0.001, is below the rigorous cut-off of p ≤ 0.01. Since such a difference could have occurred purely by chance less than once in a thousand re-sampling passes, one rejects the null hypothesis, accepts the alternate and concludes that natural gas has historically had better acceptance than the “cleaner” alternative, available for over a quarter of a century now.

Table 2. Two-sample T for NatGas vs Ethanol

SE

N Mean StDev Mean

NatGas 36 632.8 69.4 12

Ethanol 28 165 191 36

Difference = mu (NatGas) – mu (Ethanol)

Estimate for difference: 467.3

95% CI for difference: (390.0, 544.7)

T-Test of difference = 0 (vs not =): T-Value = 12.31 P-Value = 0.000 DF = 32

Boxplot of NatGas, Ethanol
Figure 2. Boxplot of NatGas, Ethanol
Annual Trend: Natural Gas and Ethanol
Figure 3. Annual Trend: Natural Gas and Ethanol

In fact, the annual trend reveals that liquid natural gas fluctuated within a fairly narrow band from about 500 trillion BTU’s to 800, presumably because final cost was linked to OPEC-engineered crude price increases. On the other hand, ethanol use took off starting 2002, surpassed LNG last year and looks to be in the early stages yet of the product life cycle. Car owners are rational enough to adopt the cleaner fuel that is domestically produced and the price of which is not directly linked to anything that happens in the Middle East.

Next, one opted for a one-way ANOVA on the continuous variable distance travelled (“miles car is driven”) against the recode of miles per gallon into the categorical “low/high” MPG class variable. The relevant hypotheses are therefore articulated as:

H0, the null hypothesis = Mean distance driven is equal regardless of car model mileage.

Ha, the alternative hypothesis = Better mileage induces car owners to use their cars more.

The derived F value (see Table 3 overleaf) suggests that the differences in distance driven across cars of different mileage categories (a mean distance of 9,511 miles historically [or at least since 1949] and 11,599 miles in years when the national average broke the 20 MPG barrier) is so great (p < 0.001) as to preclude random chance. In fact, Minitab returns an Adjusted R2 suggesting that mileage improvements explain fully 97.9% of the variance in distance Americans take their cars.

Table 3. One-way ANOVA: Mileage, MPG class

Source DF SS MS F P

Factor 1 2970837377 2970837377 5265.89 0.000

Error 114 64314988 564167

Total 115 3035152365

S = 751.1 R-Sq = 97.88% R-Sq(adj) = 97.86%

It would therefore seem that plans by the EPA to raise the nationwide fuel efficiency standard to 35 MPG and that of the nation’s most populous state, California, to require 40 MPG by 2020 (Stoffer, pp. 8-9) will be counterproductive for the goal of reducing total emissions.

Works Cited

  1. “Annual Energy Review.” 2008. Energy Information Administration, Dept. of Energy.
  2. Stoffer, Harry. “Calif. Regulators Shoot for 40-plus MPG by 2020.” Automotive News. Detroit: 2008. Vol. 82, Iss. 6296; 8-9.
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