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Integration properties of disaggregated solar, geothermal and biomass energy consumption in the U.S.
Energy Policy 39 (2011) 5474–5479
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Integration properties of disaggregated solar, geothermal and biomass energy consumption in the U.S.$ Nicholas Apergis n, Chris Tsoumas 1 Department of Banking and Financial Management, University of Piraeus, 80 Karaoli & Dimitriou Street, Piraeus 18534, Greece
a r t i c l e i n f o
a b s t r a c t
Article history: Received 1 February 2011 Accepted 10 May 2011 Available online 8 June 2011
This paper investigates the integration properties of disaggregated solar, geothermal and biomass energy consumption in the U.S. The analysis is performed for the 1989–2009 period and covers all sectors which use these types of energy, i.e., transportation, residence, industrial, electric power and commercial. The results suggest that there are differences in the order of integration depending on both the type of energy and the sector involved. Moreover, the inclusion of structural breaks traced from the regulatory changes for these energy types seem to affect the order of integration for each series. & 2011 Elsevier Ltd. All rights reserved.
Keywords: Fractional integration Disaggregated solar Geothermal and biomass energy
1. Introduction In the U.S., expenditures on energy account for about 7% of Gross Domestic Product (Sari et al., 2008). In 2002, the commercial and residential sectors together accounted for 39.4% of energy consumption, while the industrial and the transportation sectors for 33.4% and 27.2%, respectively (Ewing et al., 2007). If shocks to these types of energy consumption have persistent effects, i.e., they are stationary in their first differences or they are described as I(1) processes, then disruptions to their production and consumption will have persistent effects on the U.S. economy. In this case, a prudent energy policy would be the search of other energy sources. Such policies would be consistent with the development of alternative technologies, such as renewable energy sources and the more efficient use of the existent types of energy via implementation of demand management policies. However, if energy consumption is a stationary process, i.e., a stationary in levels, I(0), process, disruptions to consumption will be fleeting and the economy will return to its long-run equilibrium (Narayan et al., 2008). If this is the case, it makes the search for alternatives to the existent types of energy less pressing.
$ The authors wish to express their warmest thanks to two referees of this journal for their valuable comments and suggestions that tremendously increased the quality of their paper. Special thanks also go to Luis A. Gil-Alana for providing the codes that implement fractional integration with structural breaks. n Corresponding author. Tel.: þ30 210 414 2429; fax: þ30 210 414 2341. E-mail addresses: [email protected] (N. Apergis), [email protected] (C. Tsoumas). 1 Tel.: þ30 210 414 2155.
0301-4215/$ - see front matter & 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.enpol.2011.05.015
After all, renewable energy can play an important role not only in energy security, but also in reducing emissions. Specifically, Apergis et al. (2010) provide empirical evidence for the existence of a causal relationship between CO2 emissions, renewable energy consumption, and economic growth in a group of developed and developing countries. In addition, their results indicate that renewable energy does not contribute to reductions in emissions, while there is causality between renewable energy consumption and economic growth, suggesting that the expansion of renewable energy not only can reduce the dependence of foreign energy sources, but also it can minimize the risk associated with volatile oil and natural gas supplies and prices. Moreover, Apergis and Payne (2010a, b) exemplify the contribution of renewable energy consumption to economic growth for a panel of countries from Eurasia and OECD countries, respectively. Next, the empirical evidence on the stationarity of energy consumption is mixed. The results from univariate unit root tests cannot reach a consensus (Chen and Lee, 2007). Studies on panel data tests (Joyeux and Ripple, 2007; Narayan and Smyth, 2007) find evidence in favor of the presence of a unit root. Other studies, however, reach the opposite conclusion (Chen and Lee, 2007; Mishra et al., 2009). In a special strand of the literature, Hsu et al. (2008) provide mixed evidence using different regional panels. A strand of the relevant literature employs unit root testing with and without the presence of structural breaks (Altinay and Karagol, 2004; Lee and Chang, 2005; Narayanan and Smyth, 2005). Other researchers have used panel unit root tests without structural breaks (Chen and Lee, 2007; Hsu et al., 2008; Lee and Chang, 2008; Mishra et al., 2009). Finally, other studies have tested for the presence of a unit root in both the spot and futures energy prices (Elder and Serletis, 2008; Lee and Lee, 2009).
N. Apergis, C. Tsoumas / Energy Policy 39 (2011) 5474–5479
These inconclusive findings probably reflect certain limitations in the relevant literature. First, these studies focus on aggregate energy consumption and ignore the likelihood that disaggregated, i.e., sectoral, components might exhibit a different (non)stationary behavior. Yang (2000) and Sari et al. (2008) argue that different countries or industries depend differently on energy. Thus, the use of disaggregated data is expected to facilitate comparisons of relative energy behavior across different energy sources. Moreover, the analysis so far is constrained in the stationary in levels/stationary in first differences, i.e., I(0)/I(1), dichotomy. Fractionally integrated processes represent a half way house between I(0) and I(1) paradigms (Baillie, 1996). In addition, fractional integration provides a means to isolate persistence that is not consistent with either I(0) or I(1) processes. Thus, fractional integration can be considered a generalization of this dichotomy (Kumar and Okimoto, 2007). The presence of long memory is related to autocorrelation persistence. The extent of this persistence is consistent with a stationary process, but the autocorrelation takes much longer to decay than the rate associated with the parsimonious ARMA class of models. A large number of studies test for fractional integration in macroeconomic variables (Diebold and Rudebusch, 1991; Gil-Alana, 2003; Kumar and Okimoto, 2007; Assaf, 2007; Caporale and Gil-Alana, 2008). A recent study by Lean and Smyth (2009) applies, for the first time, univariate and multivariate Lagrange Multiplier (LM) tests for fractional integration proposed by Nielsen (2005) in the disaggregated petroleum consumption in the U.S. This testing methodology has the advantage that it takes into account potentially important correlations between the elements of multiple time series. This paper aims at investigating the fractional integration behavior of solar, geothermal and biomass sectoral energy consumption in the U.S. over the 1989–2009 period in the presence of structural breaks. The dates of these breaks can be traced from the following discussion of the regulatory changes in the solar, geothermal and biofuel sectors in the U.S. Our econometric specification enables us to account not only for the long memory in all three types of renewable energy consumption processes as other studies do, but also for the presence of breaks. In particular, the novelty of this empirical study by combining long memory or fractional integration and the presence of breaks or regime switching is that it gives the benefit of identifying different shocks affecting the consumption of all three types of renewable energies, depending on the regime under study. Ignoring such a combination yields misleading inferences (Diebold and Inoue, 2001; Granger and Hyung, 2004). 1.1. Solar energy In order to reduce energy consumption and pollution, adjust energy structure and achieve sustainable growth, the U.S. made a positive exploration of solar energy. The ‘A Million Solar Roofs’ plan initiated in 2006, is geared to the 21st century by the government advocacy. By implementation of this plan, solar energy technology applications will be further expanded to reduce greenhouse gas emissions and create new high-tech jobs. By 2011, the plan will not only satisfy household’s electricity needs that participated in the plan, but also they will be capable of selling electricity generated by solar energy. In addition, the Biofuels Security Act of 2006 exemplified the research, production and consumption of alternative to oil and coal types of energy, including solar energy. 1.2. Geothermal energy Geothermal energy is one of the four kinds of renewable energy sources that is used in the U.S. The others are biomass,
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hydropower and wind power. It is also the fourth largest source of energy. It amounts to 4% of electricity consumption in the U.S. and this percentage is increasing by the year. The U.S. has the largest potential to generate geothermal energy in the world and generates nearly 15 billion kilowatts of energy from it every year. There are nearly 9 western states currently producing the majority of the geothermal power required. Every year in the U.S., nearly 50 thousand geothermal heat pumps are being installed. California is the number one state for geothermal installations, while Hawaii gets 20% of its power consumption from geothermal stations. However, the largest impediment to growth is the difficulty of getting permits to develop projects on public lands. Relevant bill legislations that boosted the consumption of geothermal energy were the Solar, Wind, Waste, and Geothermal Power Production Incentives Act of 1990 and the Energy Policy Act in 1992. Moreover, the Energy Act, declared in 2005, fully equips the power generated by geothermal plants and used as a tax credit, while this was previously given for wind energy and biomass projects only. The Energy Independence and Security Act of 2007 and its extension in 2008 declared that the U.S. Senate supported programs of research, development, demonstration, and commercial application in advanced geothermal energy technologies to expand its use, while it extended certain tax incentives for businesses and homeowners to deduct part of new geothermal installations for two years. 1.3. Biofuels The OECD (2006) defines biofuels as transportations fuels derived from ‘biological sources’, while their primary outputs are ethanol and biodiesel. This type of fuels has certain advantages: First, it can be blended with gasoline or diesel, thus requiring only minor adjustments to existing engine technology and fueling infrastructure. Second, its usage may lead to lower demand for oil imports and higher national energy independence. Third, it can serve as an income source for local rural communities. A widely held view is that the U.S. biofuels industry has emerged due to the fact that unlike other forms of renewable energy, such as solar and wind, biofuels are easy for people and business to use. At the same time, new regulation has contributed significantly in promoting its expansion, mainly the 2005 Energy Policy Act along with the March 2005 introduction of a futures ethanol contract in the Chicago Board of Trade (Rajagopal et al., 2007; Banse et al., 2008). The Act refers to provisions that promote energy efficiency and conservation, as well as to modernizations of the domestic energy infrastructure, while it provides incentives for both traditional energy sources and renewable alternatives. Nevertheless, experts provide strong arguments against biofuels on the grounds that their production, in order to meet increasing demand, might require converting just about the majority of the world’s remaining forests and open spaces over to agricultural land. Other dark clouds looming over biofuels is whether producing them requires more energy levels (Pimental and Patzek, 2005), while it can increase the release of greenhouse gases relative to the fossil fuels they replace, thus aggravating global warming. Moreover, the production of cornbased ethanol nearly doubles greenhouse emissions over 30 years and increases greenhouse gases for 167 years, while biofuels made from switchgrass increase carbon emissions by 50 percent (Searchinger, 2008). However, accurately assessing the environmental impacts from using biofuels requires consideration of the entire production process. For instance, if the growing of such farmlands is based on the minimal or null use of chemical inputs, then the use of biofuels will most certainly be positive or their production offers its greatest promise for greenhouse benefits if grown on abandoned, degraded or marginal lands, since in these
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lands carbon losses from conversion to biofuels are relatively small (Howarth et al., 2009). The remainder of the paper is organized as follows: Section 2 describes the econometric methodology, while Section 3 presents the empirical results and Section 4 concludes.
2. Econometric methodology A fractional integration process has the characteristic of long memory that a parsimonious ARMA model is not adequate to describe. Thus, it requires a large number of lagged autoregressive and moving average terms to capture its autocorrelation pattern (Parke, 1999). Such a fractional integration process can be expressed as ð1LÞd xt ¼ et
ð1Þ
where L is the lag operator, et is the i.i.d. and the differencing parameter d is a real number. In the case of d ¼0, the stochastic process xt is the covariance stationary, while in the case of d ¼1, xt is a unit root non-stationary process, i.e., the model contains a stochastic trend. Fractional integration arises when d takes positive and non-integer values, i.e., d 40. In this case, xt has long memory because observations that have a long distance in time are associated (Gil-Alana, 2002). Within this setting, two interesting cases emerge: In the first, d is restricted in the (0, 0.5) interval, while in the second d Z0.5. In the former, xt continues to be a covariance stationary process, still its autocovariance function decays more slowly than in the stationary, I(0), case. In the latter, xt is non-stationary, however still mean reverting, while its autocovariance function displays higher persistence. Finally, when d Z1, xt is a non-stationary process in which the effect of a shock lasts forever. Thus, as Gil-Alana and Robinson (1997), GilAlana (1999) and Gil-Alana (2002) point out, d indicates the degree of persistence for xt: the higher the value of d, the higher the persistence. We model each energy consumption series with a known break at a certain point in time, as in Gil-Alana (2002) yt ¼ b0 þ b1 t þ b2 Dt ðTb Þ þut
ð2Þ
where b0, b1 and b2 are the unknown parameters and Dt is a dummy variable that accounts for the break in the series occurring at time Tb. This dummy variable takes the value of 1 for t ZTb and zero otherwise when the break occur in the intercept. For a break in the slope, Dt(Tb) ¼tI(t ZTb), where I is an operator that takes the value of 1 for t ZTb and zero otherwise. The error term ut has the form of ð1LÞd ut ¼ et
ð3Þ
where et is a covariance stationary process with positive and finite at the zero frequency spectral density function. as suggested by Robinson (1994), and employed in Gil-Alana and Robinson (1997) and Gil-Alana (2002), Eq. (2) is estimated with OLS and for the fractional integration test, the null hypothesis is H0 : d ¼ d0 against the one sided alternatives HA : d 4 d0 ðor d o d0 Þ The Robinson’s (1994) test statistic for a sample size T is given by r^ ¼
1=2 T a^ ^A s^ 2
where
a^ ¼
T 1 2p X cðlj Þgðlj ; t^ Þ1 Iðlj Þ T j¼1
T1 2p X gðlj ; t^ Þ1 Iðlj Þ T j¼1 1 0 0 11 T1 T1 T1 T1 X X X X 2 B 2 0 0 cðlj Þ cðlj Þe^ ðlj Þ @ e^ ðlj Þe^ ðlj Þ A e^ ðlj Þcðlj ÞC A^ ¼ @ A T j¼1 j¼1 j¼1 j¼1
s^ ¼
l cðlj Þ ¼ log2 sin j ; 2
e^ ðlj Þ ¼
@ log gðlj ; t^ Þ; @t
lj ¼
2pj T
g(lj;t) is a known smooth function, so as et is I(0) and does not have long memory. In the specific case that et is white noise, then g 1. Robinson (1994) showed that the r statistic follows a standard Normal distribution as T grows to infinity: r^ -d Nð0,1Þ
as
T-1
Thus, the null hypothesis is rejected in favor of the alternative, if r^ 4za (or r^ o za ), where a is the level of significance for the test.
3. Empirical analysis Data for solar, geothermal and biomass consumption in the U.S., measured in billion btu, come from the Energy Information Administration for the period 1989–2009 on a monthly basis. The series measure consumption for commercial, residential, electric power, industrial and transportation and are non-seasonally adjusted. Thus, we first remove the seasonal pattern by means of the U.S. Census Bureau’s X12 seasonal adjustment procedure. We employ the Gil-Alana code modeling yt as in Eq. (2) without and with a structural break, i.e., when b2 ¼0 and b2 a0, respectively. The break dates are taken from the type of energy analysis presented in the introduction. When accounting for a break, we further distinguish between two cases: first, when we model the break in the intercept, i.e., Dt ¼1 for t ZTb, and second, when we model the break in the slope, i.e., Dt ¼ I(t) for t ZTb. We test for the stationarity in levels, I(0), and stationarity in first differences, I(1), hypotheses, i.e., when d0 takes the values of 0 and 1, respectively, as well as for the fractional integration hypotheses with values of d0 within the interval (0, 1) with grid, i.e., with step, 0.1. The test is performed for both white noise and AR(1) disturbances. Because of the non-monotonic behavior of the Robinson’s r statistic for a number of series under study with AR(1) error type, evidence of a model misspecification (Gil-Alana, 2002), these results are not reported here but are available upon request. Tables 1–3 report the results for solar, geothermal, biomass energy consumption, respectively. Each table is divided into three panels: Panel A reports the results without any structural break, while Panels B and C report the results when the break is in the intercept and the slope, respectively. The first row in each panel shows the different values of d0 for which the analysis is implemented, while the first three columns list the sectors of the economy in which the specific source of energy is used, the type of the residuals used in Eq. (2), i.e., white noise or AR(1), and the date in which the break occurs. For the case of solar energy consumption in residential use and when no structural break is included in Eq. (2), the null hypothesis is not rejected for d0 ¼0.8, implying that the series exhibit a stationary behavior, though with a relatively high persistence (Table 1, Panel A). When a break is included in the intercept at 2006:12, the non-rejection value of d0 rises to 0.9 (Panel B), while
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Table 1 Fractional integration results—solar consumption. Use
Error type
Break
d0 0
0.1
37.94 6.09 1.66
37.65 3.67 1.13n
Panel B—With a structural break in the intercept Residence White noise 2006:12 47.02 Electric power White noise 25.12 AR(1) 10.68 Panel C—With a structural break in the slope Residence White noise 2006:12 22.94 Electric power White noise 6.90 AR(1) 1.97
Panel A—Without a structural break Residence White noise – Electric power White noise AR(1)
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
36.38 1.10n 0.25n
33.43 0.81n 0.61n
28.06 2.24 1.37n
20.26 3.31 2.01
11.45 4.14 2.55
3.81 4.80 3.01
1.47n 5.32 3.41
4.59 5.75 3.76
6.26 6.11 4.07
45.91 17.77 10.48
43.48 9.28 7.86
39.45 3.41 4.40
33.46 0.16n 1.58n
25.86 2.26 0.33n
17.79 3.56 1.54n
10.58 4.43 2.34
4.99 5.06 2.91
1.05n 5.55 3.36
1.59n 5.95 3.74
20.21 3.83 0.89n
16.57 0.87n 0.40n
13.10 1.15n 1.39n
9.97 2.53 2.07
7.18 3.48 2.50
4.31 4.18 2.78
0.97n 4.74 3.04
2.32 5.22 3.33
4.79 5.64 3.62
6.29 6.00 3.91
Notes: (1) Sample period: 1989:1–2009:12. (2) For all series the seasonal pattern have been removed by means of the U.S. Census Bureau’s X12 seasonal adjustment procedure. (3) An asterisk indicates the non-rejection values of the null hypothesis at the 5% level of significance.
Table 2 Fractional integration results—geothermal consumption. Use
Error type
Break
d0 0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
43.83 12.36 0.78n 33.09 45.05
42.16 9.35 0.70n 31.24 44.98
39.18 5.90 0.52n 28.23 44.54
35.18 3.13 0.06n 24.55 43.55
30.05 0.97n 0.45n 20.29 41.81
23.79 0.71n 0.94n 15.73 39.00
16.86 2.02 1.38n 11.25 34.61
10.29 3.05 1.77 7.23 28.08
4.99 3.89 2.12 3.88 19.60
1.22n 4.57 2.44 1.25n 10.72
1.32n 5.14 2.75 0.74n 3.44
Panel B—With a structural break in the intercept Commercial White noise 1990:10 42.63 2005:12 37.97 2007:12 40.26 2008:10 40.43 Electric power White noise 1990:10 31.45 2005:12 42.54 2007:12 43.34 2008:10 43.11 Industrial White noise 1990:10 27.54 2005:12 36.52 2007:12 39.07 2008:10 40.65 Residence White noise 1990:10 46.03 2005:12 40.06 2007:12 39.67 2008:10 41.82
40.55 36.36 38.55 38.37 28.85 39.93 40.89 40.77 26.02 34.07 36.49 38.25 45.96 39.17 38.05 40.62
36.84 33.72 35.54 34.79 25.09 35.08 36.13 36.14 23.82 30.34 32.29 33.98 45.55 38.26 36.02 38.80
31.97 30.29 31.51 30.18 21.02 28.60 29.52 29.60 21.39 26.10 27.35 28.56 44.55 37.62 34.31 36.78
26.12 25.84 26.41 24.69 16.58 21.08 21.70 21.78 18.52 21.38 21.98 22.47 42.57 36.89 32.89 34.56
19.80 20.39 20.48 18.79 11.82 13.74 14.07 14.11 15.03 16.38 16.60 16.49 39.06 35.21 31.19 31.82
13.70 14.45 14.38 13.09 7.16 7.70 7.84 7.85 11.07 11.53 11.61 11.27 33.47 31.43 28.20 27.93
8.39 8.89 8.86 8.11 3.23 3.30 3.36 3.35 7.19 7.31 7.34 7.06 25.82 25.03 23.08 22.36
1.02n 1.04n 1.11n 1.02n 1.74 1.75 1.74 1.75 1.33n 1.34n 1.32n 1.31n 9.31 9.31 9.21 8.66
1.24n 1.3n 1.21n 1.21n 3.16 3.17 3.16 3.17 0.61n 0.6n 0.62n 0.58n 3.34 3.37 3.50 3.15
Panel C—With a structural break in the slope Commercial White noise 1990:10 43.81 2005:12 42.85 2007:12 44.65 2008:10 45.04 Electric power White noise 1990:10 12.35 2005:12 13.70 2007:12 13.58 2008:10 13.58 Industrial White noise 1990:10 32.61 2005:12 43.73 2007:12 43.78 2008:10 42.70 Residence White noise 1990:10 45.03 2005:12 30.51 2007:12 38.57 2008:10 40.41
42.28 41.07 43.50 44.18 9.35 10.19 10.07 10.08 30.79 41.71 42.05 40.99 44.92 28.28 36.32 39.00
39.56 37.96 41.30 42.42 5.90 6.29 6.20 6.22 27.82 37.99 38.79 37.81 44.44 25.37 32.65 36.47
35.82 34.01 38.21 39.79 3.13 3.28 3.21 3.23 24.19 32.86 34.09 33.25 43.40 22.74 28.48 33.19
30.84 29.46 34.24 36.16 0.97n 1.01n 0.97n 0.98n 19.99 26.30 27.68 26.99 41.57 20.78 24.47 29.47
24.59 24.89 29.58 31.38 0.71n 0.70n 0.72n 0.72n 15.49 18.96 20.01 19.39 38.57 20.14 21.68 26.11
17.63 20.51 23.91 24.70 2.02 2.02 2.02 2.03 11.07 12.32 12.84 12.3 33.89 20.95 20.73 23.71
11.07 14.93 16.18 15.64 3.06 3.05 3.05 3.06 7.10 7.40 7.58 7.24 27.16 21.21 20.02 21.20
1.66 2.04 2.04 1.71 4.58 4.57 4.57 4.57 1.21n 1.26n 1.23n 1.27n 10.63 10.49 10.12 9.47
1.25n 1.38n 1.29n 1.36n 5.14 5.14 5.14 5.14 0.75n 0.72n 0.76n 0.66n 3.42 3.25 3.39 2.79
Panel A—Without a structural break Commercial White noise – Electric power White noise AR(1) Industrial White noise Residence White noise
4.17 4.37 4.41 4.08 0.31n 0.29n 0.31n 0.31n 3.90 3.92 3.92 3.80 17.19 17.00 16.22 15.48 5.72 7.82 7.92 7.17 3.89 3.89 3.88 3.89 3.80 3.88 3.91 3.80 18.96 17.66 16.58 16.42
Notes: Similar to Table 1.
when the break is in the slope, then it drops to 0.7 (Panel C). By contrast, for the case of electric power consumption, when no break is included, the non-rejection values of d0 are 0.2 and 0.3 for
the white noise error type, while for the AR(1) case, they turn to be 0.1 and 0.4. When a break in the intercept is considered at 2006:12, the value of d0 rises to 0.4 and 0.6, for the two error types,
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Table 3 Fractional integration results—biomass consumption. Use
Error type
Break
d0 0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
45.45 47.25 16.42 4.65 27.84 35.4
45.10 46.49 12.05 4.64 23.50 32.38
43.92 44.78 7.03 3.15 17.42 27.22
41.51 41.93 3.21 1.27n 11.65 20.94
37.30 37.55 0.49n 0.47n 6.70 14.25
30.76 31.31 1.39n 1.87 2.73 8.14
22.02 23.49 2.69 2.92 0.29n 3.33
12.64 15.33 3.60 3.70 2.46 0.05n
4.83 8.40 4.26 4.28 3.91 2.29
0.37n 3.41 4.77 4.72 4.86 3.75
3.39 0.14n 5.18 5.08 5.49 4.72
Panel B—With a structural break in the intercept Transportation White noise 2006:1 29.65 Residence 40.50 Industrial 42.20 Electric power 44.27 Commercial 43.87 Transportation White noise 2006:1 27.19 Residence 45.89 Industrial 14.71 Electric power 27.25 Commercial 34.74
29.04 38.80 39.42 42.23 41.96 25.12 44.60 10.53 22.80 31.77
28.22 36.15 34.29 38.07 38.39 22.31 42.25 5.93 16.50 26.65
27.27 33.03 27.56 31.68 33.31 19.58 39.03 2.51 10.40 20.44
25.76 29.21 19.98 23.20 26.67 17.16 34.92 0.09n 5.17 13.88
22.80 24.30 12.85 14.14 19.08 15.16 29.92 1.60n 1.29n 7.93
17.68 18.28 7.14 6.55 11.81 13.00 23.63 2.78 1.23n 3.24
11.02 12.05 3.03 1.34n 5.93 9.35 16.22 3.63 2.87 0.08n
4.66 6.76 0.23n 1.84 1.71 4.33 9.61 4.27 4.05 2.30
0.06n 2.89 1.69 3.71 1.12n 0.18n 4.70 4.77 4.89 3.75
3.00 0.25n 3.02 4.85 3.00 3.15 1.29n 5.17 5.49 4.71
Panel A—Without a structural break Transportation White noise – Residence White noise Industrial White noise AR(1) Electric power White noise Commercial White noise
Notes: Similar to Table 1.
respectively. The results for the break in the slope for this series, reported in Panel C, are essentially the same as in the no-break case. Overall, the electric power consumption seems to exhibit a less persistent behavior than the residential use of solar energy. For geothermal energy consumption, Panel A in Table 2 indicates that for the cases of commercial and industrial use the nonrejection values of H0 are 0.9 and 1 when white noise disturbances and no structural break are implemented. For the case of electric power consumption, the relevant values of d0 drop to 0.4 and 0.5 for the case of white noise and to 0-0.6 for the case of AR(1) error type, respectively. In contrast, for the case of residential use, all d0 values in the [0, 1] interval reject the null hypothesis, while the decreasing behavior of the statistic possibly indicates that this series is fractionally integrated of higher order. When a break in the intercept or the slope is included in the model at 1990:10, 2005:12, 2007:12, and 2008:10, a similar picture is painted. In the case where the break is in the intercept (Panel B), the nonrejection values are the same for all break dates. For the cases of commercial and industrial use, the findings are similar as in the no break case, while for the case of electric power consumption, the value rises to 0.8 and for the case of residential use, it is possibly higher than 1. Finally, the relevant values for a break in the slope (Panel C) are the same as when no break is considered. Finally, as Table 3 shows, for biomass consumption and for the case of transportation use considering a break at 2006:1 seems irrelevant, as the non-rejection value of d0 is 0.9 in the no break case and in the break cases, irrespectively of the way this break is being modeled. The same holds for the case of residential use, where the relevant value is 1, i.e., this series is I(1). For the case of industrial use, the values of d0 for which the null is not rejected are in the neighborhood of 0.4, when no break occurs or when a break at 2006:1 in the slope is being modeled, and 0.8 when the break is in the intercept. As for the cases of electric power and commercial use without a break, the order of integration is 0.6 and 0.7, respectively (Panel A), 0.7 and 0.9 with a break in the intercept (Panel B), and 0.6 and 0.7 again with a break in the intercept (Panel C).
4. Conclusions and policy implications The increasing importance of renewable energy sources, both as alternatives to traditional ones and tools for more effective
environmental policies have attracted a lot of attention by academics and policy makers. In this paper we examined the integration properties of disaggregated solar, geothermal and biomass energy consumption in the U.S. The results suggest that there are differences in the order of integration depending on both the type of energy and the sector involved. The inclusion of structural breaks traced from the regulatory changes for these energy types seem to affect the order of integration for each series. However, in all cases the order of integration found to be less than 1, indicating that energy conservation policies for these types – defined as a shock – will be transitory. Moreover, since these types of energy consumption and for the various sectors do not contain a unit root, then it is possible to forecast future movements based on their past behavior. The empirical results have significant policy implications about the impact of shocks on the consumption of all three types of renewable energy and in all sectors under study. In particular, the transitory nature of the regulatory changes – shocks – for these energy types implies that these changes may not have serious impact on certain dimensions of the real economy, such as output and employment. The results also cast serious doubt about the future of the renewable energy sector, probably because of the negative characteristics of certain renewable markets, mentioned in the introductory section. This implies that policy makers should provide the appropriate incentive mechanism for the introduction of a friendly to the environment technology used in those markets, such as tax credits and/or subsidies. In addition, the transitory character of shocks discourages demand stabilization policies, either in the aggregate economy or in specific sectors. After all, the massive fiscal interventions following the recent financial crisis have led to a deterioration of the country’s future debt picture.
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Energy Policy journal homepage: www.elsevier.com/locate/enpol
Integration properties of disaggregated solar, geothermal and biomass energy consumption in the U.S.$ Nicholas Apergis n, Chris Tsoumas 1 Department of Banking and Financial Management, University of Piraeus, 80 Karaoli & Dimitriou Street, Piraeus 18534, Greece
a r t i c l e i n f o
a b s t r a c t
Article history: Received 1 February 2011 Accepted 10 May 2011 Available online 8 June 2011
This paper investigates the integration properties of disaggregated solar, geothermal and biomass energy consumption in the U.S. The analysis is performed for the 1989–2009 period and covers all sectors which use these types of energy, i.e., transportation, residence, industrial, electric power and commercial. The results suggest that there are differences in the order of integration depending on both the type of energy and the sector involved. Moreover, the inclusion of structural breaks traced from the regulatory changes for these energy types seem to affect the order of integration for each series. & 2011 Elsevier Ltd. All rights reserved.
Keywords: Fractional integration Disaggregated solar Geothermal and biomass energy
1. Introduction In the U.S., expenditures on energy account for about 7% of Gross Domestic Product (Sari et al., 2008). In 2002, the commercial and residential sectors together accounted for 39.4% of energy consumption, while the industrial and the transportation sectors for 33.4% and 27.2%, respectively (Ewing et al., 2007). If shocks to these types of energy consumption have persistent effects, i.e., they are stationary in their first differences or they are described as I(1) processes, then disruptions to their production and consumption will have persistent effects on the U.S. economy. In this case, a prudent energy policy would be the search of other energy sources. Such policies would be consistent with the development of alternative technologies, such as renewable energy sources and the more efficient use of the existent types of energy via implementation of demand management policies. However, if energy consumption is a stationary process, i.e., a stationary in levels, I(0), process, disruptions to consumption will be fleeting and the economy will return to its long-run equilibrium (Narayan et al., 2008). If this is the case, it makes the search for alternatives to the existent types of energy less pressing.
$ The authors wish to express their warmest thanks to two referees of this journal for their valuable comments and suggestions that tremendously increased the quality of their paper. Special thanks also go to Luis A. Gil-Alana for providing the codes that implement fractional integration with structural breaks. n Corresponding author. Tel.: þ30 210 414 2429; fax: þ30 210 414 2341. E-mail addresses: [email protected] (N. Apergis), [email protected] (C. Tsoumas). 1 Tel.: þ30 210 414 2155.
0301-4215/$ - see front matter & 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.enpol.2011.05.015
After all, renewable energy can play an important role not only in energy security, but also in reducing emissions. Specifically, Apergis et al. (2010) provide empirical evidence for the existence of a causal relationship between CO2 emissions, renewable energy consumption, and economic growth in a group of developed and developing countries. In addition, their results indicate that renewable energy does not contribute to reductions in emissions, while there is causality between renewable energy consumption and economic growth, suggesting that the expansion of renewable energy not only can reduce the dependence of foreign energy sources, but also it can minimize the risk associated with volatile oil and natural gas supplies and prices. Moreover, Apergis and Payne (2010a, b) exemplify the contribution of renewable energy consumption to economic growth for a panel of countries from Eurasia and OECD countries, respectively. Next, the empirical evidence on the stationarity of energy consumption is mixed. The results from univariate unit root tests cannot reach a consensus (Chen and Lee, 2007). Studies on panel data tests (Joyeux and Ripple, 2007; Narayan and Smyth, 2007) find evidence in favor of the presence of a unit root. Other studies, however, reach the opposite conclusion (Chen and Lee, 2007; Mishra et al., 2009). In a special strand of the literature, Hsu et al. (2008) provide mixed evidence using different regional panels. A strand of the relevant literature employs unit root testing with and without the presence of structural breaks (Altinay and Karagol, 2004; Lee and Chang, 2005; Narayanan and Smyth, 2005). Other researchers have used panel unit root tests without structural breaks (Chen and Lee, 2007; Hsu et al., 2008; Lee and Chang, 2008; Mishra et al., 2009). Finally, other studies have tested for the presence of a unit root in both the spot and futures energy prices (Elder and Serletis, 2008; Lee and Lee, 2009).
N. Apergis, C. Tsoumas / Energy Policy 39 (2011) 5474–5479
These inconclusive findings probably reflect certain limitations in the relevant literature. First, these studies focus on aggregate energy consumption and ignore the likelihood that disaggregated, i.e., sectoral, components might exhibit a different (non)stationary behavior. Yang (2000) and Sari et al. (2008) argue that different countries or industries depend differently on energy. Thus, the use of disaggregated data is expected to facilitate comparisons of relative energy behavior across different energy sources. Moreover, the analysis so far is constrained in the stationary in levels/stationary in first differences, i.e., I(0)/I(1), dichotomy. Fractionally integrated processes represent a half way house between I(0) and I(1) paradigms (Baillie, 1996). In addition, fractional integration provides a means to isolate persistence that is not consistent with either I(0) or I(1) processes. Thus, fractional integration can be considered a generalization of this dichotomy (Kumar and Okimoto, 2007). The presence of long memory is related to autocorrelation persistence. The extent of this persistence is consistent with a stationary process, but the autocorrelation takes much longer to decay than the rate associated with the parsimonious ARMA class of models. A large number of studies test for fractional integration in macroeconomic variables (Diebold and Rudebusch, 1991; Gil-Alana, 2003; Kumar and Okimoto, 2007; Assaf, 2007; Caporale and Gil-Alana, 2008). A recent study by Lean and Smyth (2009) applies, for the first time, univariate and multivariate Lagrange Multiplier (LM) tests for fractional integration proposed by Nielsen (2005) in the disaggregated petroleum consumption in the U.S. This testing methodology has the advantage that it takes into account potentially important correlations between the elements of multiple time series. This paper aims at investigating the fractional integration behavior of solar, geothermal and biomass sectoral energy consumption in the U.S. over the 1989–2009 period in the presence of structural breaks. The dates of these breaks can be traced from the following discussion of the regulatory changes in the solar, geothermal and biofuel sectors in the U.S. Our econometric specification enables us to account not only for the long memory in all three types of renewable energy consumption processes as other studies do, but also for the presence of breaks. In particular, the novelty of this empirical study by combining long memory or fractional integration and the presence of breaks or regime switching is that it gives the benefit of identifying different shocks affecting the consumption of all three types of renewable energies, depending on the regime under study. Ignoring such a combination yields misleading inferences (Diebold and Inoue, 2001; Granger and Hyung, 2004). 1.1. Solar energy In order to reduce energy consumption and pollution, adjust energy structure and achieve sustainable growth, the U.S. made a positive exploration of solar energy. The ‘A Million Solar Roofs’ plan initiated in 2006, is geared to the 21st century by the government advocacy. By implementation of this plan, solar energy technology applications will be further expanded to reduce greenhouse gas emissions and create new high-tech jobs. By 2011, the plan will not only satisfy household’s electricity needs that participated in the plan, but also they will be capable of selling electricity generated by solar energy. In addition, the Biofuels Security Act of 2006 exemplified the research, production and consumption of alternative to oil and coal types of energy, including solar energy. 1.2. Geothermal energy Geothermal energy is one of the four kinds of renewable energy sources that is used in the U.S. The others are biomass,
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hydropower and wind power. It is also the fourth largest source of energy. It amounts to 4% of electricity consumption in the U.S. and this percentage is increasing by the year. The U.S. has the largest potential to generate geothermal energy in the world and generates nearly 15 billion kilowatts of energy from it every year. There are nearly 9 western states currently producing the majority of the geothermal power required. Every year in the U.S., nearly 50 thousand geothermal heat pumps are being installed. California is the number one state for geothermal installations, while Hawaii gets 20% of its power consumption from geothermal stations. However, the largest impediment to growth is the difficulty of getting permits to develop projects on public lands. Relevant bill legislations that boosted the consumption of geothermal energy were the Solar, Wind, Waste, and Geothermal Power Production Incentives Act of 1990 and the Energy Policy Act in 1992. Moreover, the Energy Act, declared in 2005, fully equips the power generated by geothermal plants and used as a tax credit, while this was previously given for wind energy and biomass projects only. The Energy Independence and Security Act of 2007 and its extension in 2008 declared that the U.S. Senate supported programs of research, development, demonstration, and commercial application in advanced geothermal energy technologies to expand its use, while it extended certain tax incentives for businesses and homeowners to deduct part of new geothermal installations for two years. 1.3. Biofuels The OECD (2006) defines biofuels as transportations fuels derived from ‘biological sources’, while their primary outputs are ethanol and biodiesel. This type of fuels has certain advantages: First, it can be blended with gasoline or diesel, thus requiring only minor adjustments to existing engine technology and fueling infrastructure. Second, its usage may lead to lower demand for oil imports and higher national energy independence. Third, it can serve as an income source for local rural communities. A widely held view is that the U.S. biofuels industry has emerged due to the fact that unlike other forms of renewable energy, such as solar and wind, biofuels are easy for people and business to use. At the same time, new regulation has contributed significantly in promoting its expansion, mainly the 2005 Energy Policy Act along with the March 2005 introduction of a futures ethanol contract in the Chicago Board of Trade (Rajagopal et al., 2007; Banse et al., 2008). The Act refers to provisions that promote energy efficiency and conservation, as well as to modernizations of the domestic energy infrastructure, while it provides incentives for both traditional energy sources and renewable alternatives. Nevertheless, experts provide strong arguments against biofuels on the grounds that their production, in order to meet increasing demand, might require converting just about the majority of the world’s remaining forests and open spaces over to agricultural land. Other dark clouds looming over biofuels is whether producing them requires more energy levels (Pimental and Patzek, 2005), while it can increase the release of greenhouse gases relative to the fossil fuels they replace, thus aggravating global warming. Moreover, the production of cornbased ethanol nearly doubles greenhouse emissions over 30 years and increases greenhouse gases for 167 years, while biofuels made from switchgrass increase carbon emissions by 50 percent (Searchinger, 2008). However, accurately assessing the environmental impacts from using biofuels requires consideration of the entire production process. For instance, if the growing of such farmlands is based on the minimal or null use of chemical inputs, then the use of biofuels will most certainly be positive or their production offers its greatest promise for greenhouse benefits if grown on abandoned, degraded or marginal lands, since in these
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lands carbon losses from conversion to biofuels are relatively small (Howarth et al., 2009). The remainder of the paper is organized as follows: Section 2 describes the econometric methodology, while Section 3 presents the empirical results and Section 4 concludes.
2. Econometric methodology A fractional integration process has the characteristic of long memory that a parsimonious ARMA model is not adequate to describe. Thus, it requires a large number of lagged autoregressive and moving average terms to capture its autocorrelation pattern (Parke, 1999). Such a fractional integration process can be expressed as ð1LÞd xt ¼ et
ð1Þ
where L is the lag operator, et is the i.i.d. and the differencing parameter d is a real number. In the case of d ¼0, the stochastic process xt is the covariance stationary, while in the case of d ¼1, xt is a unit root non-stationary process, i.e., the model contains a stochastic trend. Fractional integration arises when d takes positive and non-integer values, i.e., d 40. In this case, xt has long memory because observations that have a long distance in time are associated (Gil-Alana, 2002). Within this setting, two interesting cases emerge: In the first, d is restricted in the (0, 0.5) interval, while in the second d Z0.5. In the former, xt continues to be a covariance stationary process, still its autocovariance function decays more slowly than in the stationary, I(0), case. In the latter, xt is non-stationary, however still mean reverting, while its autocovariance function displays higher persistence. Finally, when d Z1, xt is a non-stationary process in which the effect of a shock lasts forever. Thus, as Gil-Alana and Robinson (1997), GilAlana (1999) and Gil-Alana (2002) point out, d indicates the degree of persistence for xt: the higher the value of d, the higher the persistence. We model each energy consumption series with a known break at a certain point in time, as in Gil-Alana (2002) yt ¼ b0 þ b1 t þ b2 Dt ðTb Þ þut
ð2Þ
where b0, b1 and b2 are the unknown parameters and Dt is a dummy variable that accounts for the break in the series occurring at time Tb. This dummy variable takes the value of 1 for t ZTb and zero otherwise when the break occur in the intercept. For a break in the slope, Dt(Tb) ¼tI(t ZTb), where I is an operator that takes the value of 1 for t ZTb and zero otherwise. The error term ut has the form of ð1LÞd ut ¼ et
ð3Þ
where et is a covariance stationary process with positive and finite at the zero frequency spectral density function. as suggested by Robinson (1994), and employed in Gil-Alana and Robinson (1997) and Gil-Alana (2002), Eq. (2) is estimated with OLS and for the fractional integration test, the null hypothesis is H0 : d ¼ d0 against the one sided alternatives HA : d 4 d0 ðor d o d0 Þ The Robinson’s (1994) test statistic for a sample size T is given by r^ ¼
1=2 T a^ ^A s^ 2
where
a^ ¼
T 1 2p X cðlj Þgðlj ; t^ Þ1 Iðlj Þ T j¼1
T1 2p X gðlj ; t^ Þ1 Iðlj Þ T j¼1 1 0 0 11 T1 T1 T1 T1 X X X X 2 B 2 0 0 cðlj Þ cðlj Þe^ ðlj Þ @ e^ ðlj Þe^ ðlj Þ A e^ ðlj Þcðlj ÞC A^ ¼ @ A T j¼1 j¼1 j¼1 j¼1
s^ ¼
l cðlj Þ ¼ log2 sin j ; 2
e^ ðlj Þ ¼
@ log gðlj ; t^ Þ; @t
lj ¼
2pj T
g(lj;t) is a known smooth function, so as et is I(0) and does not have long memory. In the specific case that et is white noise, then g 1. Robinson (1994) showed that the r statistic follows a standard Normal distribution as T grows to infinity: r^ -d Nð0,1Þ
as
T-1
Thus, the null hypothesis is rejected in favor of the alternative, if r^ 4za (or r^ o za ), where a is the level of significance for the test.
3. Empirical analysis Data for solar, geothermal and biomass consumption in the U.S., measured in billion btu, come from the Energy Information Administration for the period 1989–2009 on a monthly basis. The series measure consumption for commercial, residential, electric power, industrial and transportation and are non-seasonally adjusted. Thus, we first remove the seasonal pattern by means of the U.S. Census Bureau’s X12 seasonal adjustment procedure. We employ the Gil-Alana code modeling yt as in Eq. (2) without and with a structural break, i.e., when b2 ¼0 and b2 a0, respectively. The break dates are taken from the type of energy analysis presented in the introduction. When accounting for a break, we further distinguish between two cases: first, when we model the break in the intercept, i.e., Dt ¼1 for t ZTb, and second, when we model the break in the slope, i.e., Dt ¼ I(t) for t ZTb. We test for the stationarity in levels, I(0), and stationarity in first differences, I(1), hypotheses, i.e., when d0 takes the values of 0 and 1, respectively, as well as for the fractional integration hypotheses with values of d0 within the interval (0, 1) with grid, i.e., with step, 0.1. The test is performed for both white noise and AR(1) disturbances. Because of the non-monotonic behavior of the Robinson’s r statistic for a number of series under study with AR(1) error type, evidence of a model misspecification (Gil-Alana, 2002), these results are not reported here but are available upon request. Tables 1–3 report the results for solar, geothermal, biomass energy consumption, respectively. Each table is divided into three panels: Panel A reports the results without any structural break, while Panels B and C report the results when the break is in the intercept and the slope, respectively. The first row in each panel shows the different values of d0 for which the analysis is implemented, while the first three columns list the sectors of the economy in which the specific source of energy is used, the type of the residuals used in Eq. (2), i.e., white noise or AR(1), and the date in which the break occurs. For the case of solar energy consumption in residential use and when no structural break is included in Eq. (2), the null hypothesis is not rejected for d0 ¼0.8, implying that the series exhibit a stationary behavior, though with a relatively high persistence (Table 1, Panel A). When a break is included in the intercept at 2006:12, the non-rejection value of d0 rises to 0.9 (Panel B), while
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Table 1 Fractional integration results—solar consumption. Use
Error type
Break
d0 0
0.1
37.94 6.09 1.66
37.65 3.67 1.13n
Panel B—With a structural break in the intercept Residence White noise 2006:12 47.02 Electric power White noise 25.12 AR(1) 10.68 Panel C—With a structural break in the slope Residence White noise 2006:12 22.94 Electric power White noise 6.90 AR(1) 1.97
Panel A—Without a structural break Residence White noise – Electric power White noise AR(1)
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
36.38 1.10n 0.25n
33.43 0.81n 0.61n
28.06 2.24 1.37n
20.26 3.31 2.01
11.45 4.14 2.55
3.81 4.80 3.01
1.47n 5.32 3.41
4.59 5.75 3.76
6.26 6.11 4.07
45.91 17.77 10.48
43.48 9.28 7.86
39.45 3.41 4.40
33.46 0.16n 1.58n
25.86 2.26 0.33n
17.79 3.56 1.54n
10.58 4.43 2.34
4.99 5.06 2.91
1.05n 5.55 3.36
1.59n 5.95 3.74
20.21 3.83 0.89n
16.57 0.87n 0.40n
13.10 1.15n 1.39n
9.97 2.53 2.07
7.18 3.48 2.50
4.31 4.18 2.78
0.97n 4.74 3.04
2.32 5.22 3.33
4.79 5.64 3.62
6.29 6.00 3.91
Notes: (1) Sample period: 1989:1–2009:12. (2) For all series the seasonal pattern have been removed by means of the U.S. Census Bureau’s X12 seasonal adjustment procedure. (3) An asterisk indicates the non-rejection values of the null hypothesis at the 5% level of significance.
Table 2 Fractional integration results—geothermal consumption. Use
Error type
Break
d0 0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
43.83 12.36 0.78n 33.09 45.05
42.16 9.35 0.70n 31.24 44.98
39.18 5.90 0.52n 28.23 44.54
35.18 3.13 0.06n 24.55 43.55
30.05 0.97n 0.45n 20.29 41.81
23.79 0.71n 0.94n 15.73 39.00
16.86 2.02 1.38n 11.25 34.61
10.29 3.05 1.77 7.23 28.08
4.99 3.89 2.12 3.88 19.60
1.22n 4.57 2.44 1.25n 10.72
1.32n 5.14 2.75 0.74n 3.44
Panel B—With a structural break in the intercept Commercial White noise 1990:10 42.63 2005:12 37.97 2007:12 40.26 2008:10 40.43 Electric power White noise 1990:10 31.45 2005:12 42.54 2007:12 43.34 2008:10 43.11 Industrial White noise 1990:10 27.54 2005:12 36.52 2007:12 39.07 2008:10 40.65 Residence White noise 1990:10 46.03 2005:12 40.06 2007:12 39.67 2008:10 41.82
40.55 36.36 38.55 38.37 28.85 39.93 40.89 40.77 26.02 34.07 36.49 38.25 45.96 39.17 38.05 40.62
36.84 33.72 35.54 34.79 25.09 35.08 36.13 36.14 23.82 30.34 32.29 33.98 45.55 38.26 36.02 38.80
31.97 30.29 31.51 30.18 21.02 28.60 29.52 29.60 21.39 26.10 27.35 28.56 44.55 37.62 34.31 36.78
26.12 25.84 26.41 24.69 16.58 21.08 21.70 21.78 18.52 21.38 21.98 22.47 42.57 36.89 32.89 34.56
19.80 20.39 20.48 18.79 11.82 13.74 14.07 14.11 15.03 16.38 16.60 16.49 39.06 35.21 31.19 31.82
13.70 14.45 14.38 13.09 7.16 7.70 7.84 7.85 11.07 11.53 11.61 11.27 33.47 31.43 28.20 27.93
8.39 8.89 8.86 8.11 3.23 3.30 3.36 3.35 7.19 7.31 7.34 7.06 25.82 25.03 23.08 22.36
1.02n 1.04n 1.11n 1.02n 1.74 1.75 1.74 1.75 1.33n 1.34n 1.32n 1.31n 9.31 9.31 9.21 8.66
1.24n 1.3n 1.21n 1.21n 3.16 3.17 3.16 3.17 0.61n 0.6n 0.62n 0.58n 3.34 3.37 3.50 3.15
Panel C—With a structural break in the slope Commercial White noise 1990:10 43.81 2005:12 42.85 2007:12 44.65 2008:10 45.04 Electric power White noise 1990:10 12.35 2005:12 13.70 2007:12 13.58 2008:10 13.58 Industrial White noise 1990:10 32.61 2005:12 43.73 2007:12 43.78 2008:10 42.70 Residence White noise 1990:10 45.03 2005:12 30.51 2007:12 38.57 2008:10 40.41
42.28 41.07 43.50 44.18 9.35 10.19 10.07 10.08 30.79 41.71 42.05 40.99 44.92 28.28 36.32 39.00
39.56 37.96 41.30 42.42 5.90 6.29 6.20 6.22 27.82 37.99 38.79 37.81 44.44 25.37 32.65 36.47
35.82 34.01 38.21 39.79 3.13 3.28 3.21 3.23 24.19 32.86 34.09 33.25 43.40 22.74 28.48 33.19
30.84 29.46 34.24 36.16 0.97n 1.01n 0.97n 0.98n 19.99 26.30 27.68 26.99 41.57 20.78 24.47 29.47
24.59 24.89 29.58 31.38 0.71n 0.70n 0.72n 0.72n 15.49 18.96 20.01 19.39 38.57 20.14 21.68 26.11
17.63 20.51 23.91 24.70 2.02 2.02 2.02 2.03 11.07 12.32 12.84 12.3 33.89 20.95 20.73 23.71
11.07 14.93 16.18 15.64 3.06 3.05 3.05 3.06 7.10 7.40 7.58 7.24 27.16 21.21 20.02 21.20
1.66 2.04 2.04 1.71 4.58 4.57 4.57 4.57 1.21n 1.26n 1.23n 1.27n 10.63 10.49 10.12 9.47
1.25n 1.38n 1.29n 1.36n 5.14 5.14 5.14 5.14 0.75n 0.72n 0.76n 0.66n 3.42 3.25 3.39 2.79
Panel A—Without a structural break Commercial White noise – Electric power White noise AR(1) Industrial White noise Residence White noise
4.17 4.37 4.41 4.08 0.31n 0.29n 0.31n 0.31n 3.90 3.92 3.92 3.80 17.19 17.00 16.22 15.48 5.72 7.82 7.92 7.17 3.89 3.89 3.88 3.89 3.80 3.88 3.91 3.80 18.96 17.66 16.58 16.42
Notes: Similar to Table 1.
when the break is in the slope, then it drops to 0.7 (Panel C). By contrast, for the case of electric power consumption, when no break is included, the non-rejection values of d0 are 0.2 and 0.3 for
the white noise error type, while for the AR(1) case, they turn to be 0.1 and 0.4. When a break in the intercept is considered at 2006:12, the value of d0 rises to 0.4 and 0.6, for the two error types,
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Table 3 Fractional integration results—biomass consumption. Use
Error type
Break
d0 0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
45.45 47.25 16.42 4.65 27.84 35.4
45.10 46.49 12.05 4.64 23.50 32.38
43.92 44.78 7.03 3.15 17.42 27.22
41.51 41.93 3.21 1.27n 11.65 20.94
37.30 37.55 0.49n 0.47n 6.70 14.25
30.76 31.31 1.39n 1.87 2.73 8.14
22.02 23.49 2.69 2.92 0.29n 3.33
12.64 15.33 3.60 3.70 2.46 0.05n
4.83 8.40 4.26 4.28 3.91 2.29
0.37n 3.41 4.77 4.72 4.86 3.75
3.39 0.14n 5.18 5.08 5.49 4.72
Panel B—With a structural break in the intercept Transportation White noise 2006:1 29.65 Residence 40.50 Industrial 42.20 Electric power 44.27 Commercial 43.87 Transportation White noise 2006:1 27.19 Residence 45.89 Industrial 14.71 Electric power 27.25 Commercial 34.74
29.04 38.80 39.42 42.23 41.96 25.12 44.60 10.53 22.80 31.77
28.22 36.15 34.29 38.07 38.39 22.31 42.25 5.93 16.50 26.65
27.27 33.03 27.56 31.68 33.31 19.58 39.03 2.51 10.40 20.44
25.76 29.21 19.98 23.20 26.67 17.16 34.92 0.09n 5.17 13.88
22.80 24.30 12.85 14.14 19.08 15.16 29.92 1.60n 1.29n 7.93
17.68 18.28 7.14 6.55 11.81 13.00 23.63 2.78 1.23n 3.24
11.02 12.05 3.03 1.34n 5.93 9.35 16.22 3.63 2.87 0.08n
4.66 6.76 0.23n 1.84 1.71 4.33 9.61 4.27 4.05 2.30
0.06n 2.89 1.69 3.71 1.12n 0.18n 4.70 4.77 4.89 3.75
3.00 0.25n 3.02 4.85 3.00 3.15 1.29n 5.17 5.49 4.71
Panel A—Without a structural break Transportation White noise – Residence White noise Industrial White noise AR(1) Electric power White noise Commercial White noise
Notes: Similar to Table 1.
respectively. The results for the break in the slope for this series, reported in Panel C, are essentially the same as in the no-break case. Overall, the electric power consumption seems to exhibit a less persistent behavior than the residential use of solar energy. For geothermal energy consumption, Panel A in Table 2 indicates that for the cases of commercial and industrial use the nonrejection values of H0 are 0.9 and 1 when white noise disturbances and no structural break are implemented. For the case of electric power consumption, the relevant values of d0 drop to 0.4 and 0.5 for the case of white noise and to 0-0.6 for the case of AR(1) error type, respectively. In contrast, for the case of residential use, all d0 values in the [0, 1] interval reject the null hypothesis, while the decreasing behavior of the statistic possibly indicates that this series is fractionally integrated of higher order. When a break in the intercept or the slope is included in the model at 1990:10, 2005:12, 2007:12, and 2008:10, a similar picture is painted. In the case where the break is in the intercept (Panel B), the nonrejection values are the same for all break dates. For the cases of commercial and industrial use, the findings are similar as in the no break case, while for the case of electric power consumption, the value rises to 0.8 and for the case of residential use, it is possibly higher than 1. Finally, the relevant values for a break in the slope (Panel C) are the same as when no break is considered. Finally, as Table 3 shows, for biomass consumption and for the case of transportation use considering a break at 2006:1 seems irrelevant, as the non-rejection value of d0 is 0.9 in the no break case and in the break cases, irrespectively of the way this break is being modeled. The same holds for the case of residential use, where the relevant value is 1, i.e., this series is I(1). For the case of industrial use, the values of d0 for which the null is not rejected are in the neighborhood of 0.4, when no break occurs or when a break at 2006:1 in the slope is being modeled, and 0.8 when the break is in the intercept. As for the cases of electric power and commercial use without a break, the order of integration is 0.6 and 0.7, respectively (Panel A), 0.7 and 0.9 with a break in the intercept (Panel B), and 0.6 and 0.7 again with a break in the intercept (Panel C).
4. Conclusions and policy implications The increasing importance of renewable energy sources, both as alternatives to traditional ones and tools for more effective
environmental policies have attracted a lot of attention by academics and policy makers. In this paper we examined the integration properties of disaggregated solar, geothermal and biomass energy consumption in the U.S. The results suggest that there are differences in the order of integration depending on both the type of energy and the sector involved. The inclusion of structural breaks traced from the regulatory changes for these energy types seem to affect the order of integration for each series. However, in all cases the order of integration found to be less than 1, indicating that energy conservation policies for these types – defined as a shock – will be transitory. Moreover, since these types of energy consumption and for the various sectors do not contain a unit root, then it is possible to forecast future movements based on their past behavior. The empirical results have significant policy implications about the impact of shocks on the consumption of all three types of renewable energy and in all sectors under study. In particular, the transitory nature of the regulatory changes – shocks – for these energy types implies that these changes may not have serious impact on certain dimensions of the real economy, such as output and employment. The results also cast serious doubt about the future of the renewable energy sector, probably because of the negative characteristics of certain renewable markets, mentioned in the introductory section. This implies that policy makers should provide the appropriate incentive mechanism for the introduction of a friendly to the environment technology used in those markets, such as tax credits and/or subsidies. In addition, the transitory character of shocks discourages demand stabilization policies, either in the aggregate economy or in specific sectors. After all, the massive fiscal interventions following the recent financial crisis have led to a deterioration of the country’s future debt picture.
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