3.3 Part II – California: AB32
3.3.3 VAR analysis
Weather
Following Keppler and M. Mansanet-Bataller (2010) we included in this work daily California temperatures. Using RAWS USA Climate Archive54 we gathered average values from eight rep- resentative weather stations (Kneeland, Lassen Lodge, Black Diamond, San Jose, La Panza, Oak Creek, Apple Valley). Daily average of this temperatures was then calculated from 29/08/2011, totalizing 563 observations, without missing information. As in the EU model, this variable is considered in the model as exogenous. For accountability of global warming effects, tempera- ture would have to be endogenous. However, this aspect would only be relevant if we had data for several decades, which is not the case.
equation for the joint significance of each other lagged endogenous variables in the equation, as well as a statistic for joint significance. The results are described in Figure 22:
Figure 22 : California prices - Granger causality tests
Data: 29/08/2011 – 08/11/2013. Dashed/continuous arrows indicate causality at 10%/5% significance.
Regarding CO2 price returns, we find significant causality from oil, gas and electricity. These are expected results in line with finding from other authors (Alberola et al. 2008, Creti et al. 2012, Aatola et al. 2013b, Lutz et al. 2013), even though these are all studies based on European data.
However, these studies often find other and differing significant causality relations towards CO2
that are not visible in our work.
In a reverse view, we find a significant impact of CO2 price returns in coal, gasoline and economic activity. The observed influence in coal is in line with the previous section regarding European data. This is possibly due to the inclusion in the AB32 market of emissions from imported elec- tricity, its energy mix (Table 1), and the high emission intensity levels (1001gCO2/kWhe55) of electricity generation with coal. The other energy variable where we find CO2 influence is on
55 Moomaw, W. et al, 2011, “Annex II: Methodology. In IPCC: Special Report on Renewable Energy Sources and Climate Change Mitigation” (ref. page 10),
http://srren.ipcc-wg3.de/report/IPCC_SRREN_Annex_II.pdf , retrieved 18/03/2013 CO2
Ele
Gas
Coal Econ
Oil Gasoline
gasoline price returns. In fact oil refining and fuel transportation are activities already included in the market, so it is an expected result. Finally, our study somewhat surprisingly finds a cau- sality relation from CO2 to the economic activity, presented as the stock index for large utility companies. This is an interesting result that falls out of this paper scope, and should be left for further studies.
3.3.3.2 Impulse-response analyses
Our most interesting results come from the impulse-response analysis. Our model requires the pre-definition of an order in which the variables affect each other contemporaneously. After this initial moment, the model runs without further assumptions. Our Cholesky order of influence takes into account the VAR Granger causality tests, the AB32 carbon market fundamentals and the current economic situation. In this model with seven endogenous variables, we propose that, in moment zero, oil is only impacted by its own innovation, then coal is influenced by oil’s innovation and its own, then natural gas, electricity, the economic activity and gasoline, follow the same logic, by the denoted order. Lastly, we consider carbon licences to be impacted by all innovations.
Variance decomposition
As referred in section 2.1.2.2, the Cholesky order is also needed when calculating the variance decomposition. We recall that this is auxiliary evidence of how much each variable contributes to each other in the model. As in the EU case, we present below, in Figure 23, the variance decomposition of carbon and electricity (others are available in the appendix section B.3).
It is visible in Figure 23, graph a), regarding CO2 variance decomposition, that over time all other variables gain an almost similar importance. On electricity (b), around periods 5 to 10, we see an increase in the proportion of the economy and gasoline roles, over the electricity itself. Oil, carbon and other variables also gain a role in the electricity variance.
Figure 23 : Variance decomposition of carbon and electricity prices - CA
However, our main purpose is to look to the effects in carbon prices, when having a shock in other variables, or, to the effects in other prices, when having a shock in carbon. These can be seen through impulse-response functions.
Overall, we obtain 49 Impulse-Response Functions (IRFs). It is neither relevant nor prudent to analyse them all, because all our choices, such as the Cholesky ordering, were contingent on our interest in CO2. Therefore, we focus on the IRFs associated with this variable.
First we look at the impact that an innovation in primary and secondary energy prices and econ- omy performance has in CCAs. This would provide us information on the origin of California carbon prices. Secondly, recalling a carbon market goal of pricing emissions, we analyse the re- sult of a CCA innovation in energy prices. Recalling that variables are in first log differences, we provide accumulated responses in the impulse-response functions for easier interpretation.
0 20 40 60 80 100
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
%
a) CO2
CO2 Economy Oil Coal Gas Electricity Gasoline
0 20 40 60 80 100
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
%
b) Electricity
CO2 response functions
Figure 24 : CO2 price returns accumulated responses to impulses in other variables - CA
-.03 -.02 -.01 .00 .01 .02 .03 .04
2 4 6 8 10 12 14 16 18 20
a) Oil impulse
-.03 -.02 -.01 .00 .01 .02 .03 .04
2 4 6 8 10 12 14 16 18 20
b) Coal impulse
-.03 -.02 -.01 .00 .01 .02 .03 .04
2 4 6 8 10 12 14 16 18 20
c) Gas impulse
-.03 -.02 -.01 .00 .01 .02 .03 .04
2 4 6 8 10 12 14 16 18 20
d) Electricity impulse
-.03 -.02 -.01 .00 .01 .02 .03 .04
2 4 6 8 10 12 14 16 18 20
e) Economy performance impulse
-.03 -.02 -.01 .00 .01 .02 .03 .04
2 4 6 8 10 12 14 16 18 20
f) Gasoline impulse
-.03 -.02 -.01 .00 .01 .02 .03 .04
2 4 6 8 10 12 14 16 18 20
g) Carbon impulse
-.03 -.02 -.01 .00 .01 .02 .03 .04
2 4 6 8 10 12 14 16 18 20
a) Oil impulse
-.03 -.02 -.01 .00 .01 .02 .03 .04
2 4 6 8 10 12 14 16 18 20
b) Coal impulse
-.03 -.02 -.01 .00 .01 .02 .03 .04
2 4 6 8 10 12 14 16 18 20
c) Gas impulse
-.03 -.02 -.01 .00 .01 .02 .03 .04
2 4 6 8 10 12 14 16 18 20
d) Electricity impulse
-.03 -.02 -.01 .00 .01 .02 .03 .04
2 4 6 8 10 12 14 16 18 20
e) Economy performance impulse
-.03 -.02 -.01 .00 .01 .02 .03 .04
2 4 6 8 10 12 14 16 18 20
f) Gasoline impulse
-.03 -.02 -.01 .00 .01 .02 .03 .04
2 4 6 8 10 12 14 16 18 20
g) Carbon impulse
(Blue line is the IRF; red dashed lines the limits of the 95% confidence interval. Grey circles indicate significance, whenever the function has both significative and non-significative values.)
Figure 24 represents the accumulated response of CO2 to one standard deviation innovations in each variable. The last graph (g) in Figure 24 shows the response of a CO2 impulse to itself. In the first three graphs (a,b,c) we see the impact in CO2 of innovations in primary energy variables.
All of them have periods of significative influence. Looking at natural gas we find a positive sig- nificative impact in the 3rd and 4th day after the innovation. In the next days we don’t find signif- icant results. It is a result that also occurs in Europe (Fezzi and Bunn 2009), and consistent with emission market notion that when gas prices increase there is an incentive to produce electricity with other fossil fuel, namely coal, that is more emission intensive, thus requiring more emission allowances for the same quantity of electricity produced. This is the idea behind the concepts of dark and spark spreads that decision makers consider when choosing the energy generation mix56. However, coal impulse response function does not follow this rationale. In this graph, only in the first period, we see a marginally significant response of CO2 to an impulse in coal. Although very small, the response is surprisingly positive, meaning that a positive impulse in coal prices has a CO2 response of the same signal. This is possibly related to the indirect connection indicated in Figure 22 where the coal impact reaches carbon prices via an indirect path, through gas prices, suggesting that, in the first instant, a rise in carbon prices would increase demand for natural gas, thus following the same reasoning mentioned before.
It is worthy of notice that an impulse from the electricity price (d) has no significative result in CO2 in any period, contradicting the Granger causality results presented earlier. This means that the causal mechanism identified earlier is not related to innovations in the electricity price, sug- gesting that electricity prices are relevant as a propagation mechanism of innovations on other variables.
In short, when looking at coal (b), gas (c) and electricity (f), in California, we are analysing the power market. It is possible to say that mostly primary energies influence carbon prices, while electricity may have an indirect impact. The result that coal, gas and electricity influence carbon prices is very well stated in previous research about European markets (Alberola et al. 2008, Fezzi and Bunn 2009, Keppler and Mansanet-Bataller 2010, Mansanet-Bataller et al. 2011,
56 Clean dark and spark spreads display the cost-efficient option for electricity generation in one period, either using coal or gas power plants, considering electricity, carbon, coal and gas prices.
-89-
Aatola et al. 2013b, a, García-Martos et al. 2013, Lutz et al. 2013). We validate the same idea for the Californian market.
Looking at oil (a) and gasoline (f) variables, more related to the transport sector, we find no significant response of CO2 prices to an impulse in gasoline, in any period. This is an expected result given that the transport sector will only be included in the emissions market in January 2015. It should be interesting to re-analyse this feature with future data. However, we suggest that the importance of the future integration of the transport sector in the carbon market may be seen via oil prices. In the oil-carbon IRF we note a significative negative response after the 16th period that does not fade over time. This result reinforces the idea that when there is a rise in the price of energies with high emission levels associated, carbon prices will decrease because emissions are automatically being reduced. Also, the oil price impact will withstand in future periods, contrasting to gas and coal impacts that disappear.
CO2 impulses
In Figure 25 we show the functions that represent the response of the several variables to inno- vations in carbon prices. The only IRF with significant results is the one associated with coal prices. The coal price reaction is coherent with the result in the causality analysis (Figure 22), and with other European causality studies (Keppler and Mansanet-Bataller 2010).The result is consistent with market fundamentals: when emission price increases, then emission intensive fuel, such as coal, is less demanded, so its price will decrease. This is a plausible justification essentially because AB32 includes imported electricity emissions, which is the main origin of coal use. So, it is likely that CCAs are, in some small level, in a day near the impulse, negatively influ- encing coal prices, considering at the same time the impact of all other variables in all periods.
However, the response becomes insignificant after the 3rd day.
-.03 -.02 -.01 .00 .01 .02
2 4 6 8 10 12 14 16 18 20
a) Coal response
-.01 .00 .01 .02 .03
2 4 6 8 10 12 14 16 18 20
b) Gas response
-.015 -.010 -.005 .000 .005 .010 .015
2 4 6 8 10 12 14 16 18 20
c) Oil response
-.02 -.01 .00 .01 .02 .03
2 4 6 8 10 12 14 16 18 20
d) Gasoline response
-.04 -.02 .00 .02 .04 .06
-.002 .000 .002 .004 .006 .008 .010
Accumulated Response to Cholesky One S.D. Innovations ± 2 S.E.
Figure 25 : Impulses in CO2 prices and accumulated responses of CA prices.
(Blue line is the IRF; red dashed lines the limits of the 95% confidence interval. Grey circles indicate significance, whenever the function has both significative and non-significative values.)
Other impulse-response functions, cycles and feedback
The following figures provide us with additional evidence on transmission mechanisms of infor- mation between different prices. Namely, between primary and final energies, previously iden- tified as relevant.
-.06 -.04 -.02 .00 .02 .04 .06
2 4 6 8 10 12 14 16 18 20
a) Electricity response to coal impulse
-.06 -.04 -.02 .00 .02 .04 .06
2 4 6 8 10 12 14 16 18 20
b) Electricity response to gas impulse
-.03 -.02 -.01 .00 .01 .02
2 4 6 8 10 12 14 16 18 20
a) Coal response
-.01 .00 .01 .02 .03
2 4 6 8 10 12 14 16 18 20
b) Gas response
-.015 -.010 -.005 .000 .005 .010 .015
2 4 6 8 10 12 14 16 18 20
c) Oil response
-.02 -.01 .00 .01 .02 .03
2 4 6 8 10 12 14 16 18 20
d) Gasoline response
-.04 -.02 .00 .02 .04 .06
2 4 6 8 10 12 14 16 18 20
e) Electricity response
-.002 .000 .002 .004 .006 .008 .010
2 4 6 8 10 12 14 16 18 20
f) Economy performance response Accumulated Response to Cholesky One S.D. Innovations ± 2 S.E.
Figure 26 : Other accumulated impulse-response functions of energy prices - CA
The only significant evidence appears to be the response of gas prices to a coal impulse.
This is visible on the single cyclical relationship we find in the Granger analysis of the AB32 mar- ket, presented in Figure 27:
Figure 27 : Other causality relations of CO2 - CA
Other interesting relation is the role of gas prices in this market. Its effect is rather scattered over other variables. This dubious relation is once again noted in the long-cycle analysis, in the following section.
-.01 .00 .01 .02 .03 .04 .05
2 4 6 8 10 12 14 16 18 20
c) Gas response to coal impulse
-.024 -.020 -.016 -.012 -.008 -.004 .000 .004 .008
2 4 6 8 10 12 14 16 18 20
d) Oil response to gas impulse