An Empirical Test for Inter-State Carbon-Dioxide Emissions
Leakage Resulting from the Regional Greenhouse Gas Initiative
Andrew G. Kindle and Daniel L. Shawhan
Rensselaer Polytechnic Institute
Michael J. Swider
New York Independent System Operator, Inc.
April 20, 2011
1
Summary
At the request of market participants, the New York Independent System Operator Inc.
(NYISO) undertook the task of developing a methodology to evaluate whether the cost
of compliance with the Regional Greenhouse Gas Initiative (RGGI) has caused an
increase in emissions in neighboring non-RGGI areas such as Pennsylvania. An increase
in emissions, sometimes termed “leakage,” can be caused by a shift in the relative
economics of generating power in RGGI and non- RGGI states. In order to test for such
an increase, the NYISO together with researchers at Rensselaer Polytechnic Institute
(RPI) developed econometric models to explain both power transfers between New York
and Pennsylvania and carbon dioxide (CO2) emissions from power plants in
Pennsylvania. These models estimate the effect on these two variables from variations
in RGGI allowance prices, electrical load, fuel prices, nitrogen oxide and sulfur dioxide
allowance prices, temperature, production from non-emitting generation, and New
England-to-New York power transfers. If, in these models, a higher RGGI allowance
price were empirically associated with higher Pennsylvania-New York power flows or
Pennsylvania emissions, that would indicate RGGI emissions leakage.
Other studies have modeled system-wide emissions leakage from the RGGI program or
considered the effect of a proposed transmission line on leakage. The difference
between these studies and ours is that the previous studies were prospective and
employed simulations, while ours is retrospective and employs statistical analysis of
historical data. Our econometric analysis does not support the hypothesis that RGGI
compliance cost in New York has caused emissions leakage. The period evaluated was
from 2008 through September 2010. Variables such as electrical loads, fuel costs, and
non-emitting generation were, as expected, all shown to have statistically significant
impacts on emissions, Pennsylvania-to-New York power transfers, or both. However,
the models were not able to show a statistically significant impact from RGGI costs on
either of these variables.
RGGI prices appear to have been too low from the start of the program in 2009 to have
a significant effect on emissions leakage from New York to Pennsylvania. It is not
possible to measure all factors that can influence power transfers or emissions, or to
represent in an econometric model the exact nature of the influence that the measured
factors exert. In the past decade CO2 emissions have trended lower in both New York
and Pennsylvania. These reductions are largely due to cyclical and secular changes in
supply and demand for electric power. Some of these changes have been large, and
likely contribute to the difficulty of detecting a subtle phenomenon such as emissions
leakage from RGGI. Differing market rules between areas can also reduce the price
arbitrage opportunities that would otherwise be available, and hence the response of
Pennsylvania-New York flows to a RGGI-induced increase in New York’s marginal cost of
generation.
2
Background
The ten Regional Greenhouse Gas Initiative (RGGI) states1 have committed to a CO2
budget and trading program aimed at stabilizing and then reducing CO2 emissions from
fossil-fueled electricity generating units having a rated capacity equal to or greater than
25 megawatts (“CO2 budget sources” or “sources”). This program went into effect on
January 1, 2009. RGGI requires all sources to ultimately possess CO2 allowances equal
to their CO2 emissions over a three-year control period.
The member states in RGGI auction off, or in some cases give away, allowances that
permit CO2 emissions in the electricity sector. Once auctioned, allowances may be resold in secondary markets. The need for these allowances increases the marginal cost
per MWh of generators having to purchase them, in proportion to their CO2 emissions
per MWh. This applies even to firms already holding enough allowances to meet their
requirements, because each allowance they have to retain reduces the number of
allowances they can sell at the prevailing allowance price in the secondary market.
If the cost of RGGI compliance causes load-serving entities in participating states to
import more power from non-participating states and provinces, and if the incremental
generation in those non-participating states produces CO2 emissions, the result is interregional emissions leakage. Any leakage would counteract emission reductions gained
in the RGGI states, thereby reducing the effectiveness of the RGGI program at lowering
total CO2 emissions. RGGI leakage can be defined as a RGGI-induced shift of electricity
production from generators subject to RGGI to CO2-emitting generators not subject to
RGGI.
The CO2 emission reductions resulting from RGGI can be decomposed into short-run and
long-run effects. Long-run effects can be changes in demand and changes in the supply
portfolio. This study does not try to estimate long-run effects because of the lack of
long-run historical data. However, sufficient data exists to look for short-run effects,
which can be supply-side or demand-side.
Short-run supply-side effects
a. In RGGI states: The need to hold one allowance for every ton of CO2 emitted
reduces emissions by raising the marginal generation costs, and hence the
offer prices, of higher-emitting generators more than those of lower-emitting
1
New York, New Jersey, Massachusetts, Maine, New Hampshire, Vermont, Connecticut, Rhode Island,
Delaware and Maryland.
3
generators and of generators not subject to the allowance requirement, such
as those in Pennsylvania. This causes higher-emitting generators subject to
the allowance requirement to be used less.
b. In neighboring jurisdictions: Since RGGI raises the offer prices of emitting
generators subject to its allowance requirement, generators not subject to
the requirement, such as those in neighboring states and provinces, are used
more. This increases their emissions. This is leakage.
Short-run demand-side effects
c. In RGGI states: By raising the offer prices of generators, RGGI raises the
market price of electricity, which induces an immediate reduction in
consumption.
d. In neighboring jurisdictions: The higher offer prices within RGGI (a form of
supply reduction) increase RGGI states’ demand for electricity from
neighboring jurisdictions. This reduces the residual supply of generation in
those jurisdictions, which may drive up the price and reduce consumption in
those jurisdictions. This would result in a reduction of emissions, and so can
be called “negative leakage.”
The short-run effects can be expected to affect emissions and power flows in proportion
to the then-current RGGI allowance price. However, the short-run demand-side effect is
reflected in load. Therefore, if one tests for an effect of the RGGI allowance price on
Pennsylvania-New York power flows or on Pennsylvania CO2 emissions, and controls for
load as one must, the type of leakage that remains to detect is short-run supply-side
leakage, which is the short-run supply-side effect in jurisdictions neighboring the RGGI
states. This is the most direct type of leakage, and it is what we attempt to detect in
recent historical data. The long-run leakage effects accumulate over the years rather
than being a daily function of the current RGGI allowance price, and attempting to
measure any of them would require a longer time period of historical data and a
different approach than the one we employ to measure short-run leakage.
Review of Previous RGGI Leakage Studies
An established strain of academic literature uses economic models to predict emissions
leakage between nations (e.g. Paltsev 2001). The phenomenon is not limited to CO2 or
to the electricity sector. Emissions leakage can occur when emissions raise the marginal
production cost of a tradable good in one region but not in another, such as when one
region has a cap-and-trade program on emissions from industry but another region does
not.
4
In the electricity sector, factors such as transmission constraints can limit emissions
leakage. Transmission thermal ratings and system stability requirements limit the
amount of electric power that can be imported into New York, regardless of short-run
economics. For example, total transfer capability for imports from the PJM
Interconnection are typically limited to a range of 2,800 to 3,660 MW.2
As discussed below, two previous studies have modeled system-wide emissions leakage
from the RGGI program, and another has considered the effect of a proposed
transmission line on leakage. The difference between the earlier studies and our study
is that earlier studies were prospective and employed simulations, while ours is
retrospective and employs statistical methods to attempt to identify actual RGGI
impacts.
ICF Study
At the request of RGGI Inc, ICF International used its Integrated Planning Model (IPM©)
to predict emissions leakage from RGGI through 2024. This model includes many input
variables including the costs of different types of new power plants and power plant
retrofits, air policy specifications, resource supplies, operational factors such as
maintenance requirements, existing power plant variable costs, electricity demand, and
transmission capabilities. The IPM uses a “pipe-and-bubble” (EIPC 2010) model of the
transmission system: the US and Canada are divided into regions (eight for RGGI), there
are no transmission constraints within the regions, and there are fixed MW flow
constraints between the regions. The model uses all of these inputs to predict power
plant investment and retirement, power plant dispatch, power prices, fuel prices,
allowance prices, emissions in each region, and other future variables (ICF Consulting
2006a, pp. 10 and 12). Because the IPM predicts investment in and retirement of
generators, the ICF prediction of RGGI leakage includes not just short-run supply-side
leakage but also the long-run supply-side effects. The model also includes demand-side
effects.3
The ICF study compared a business-as-usual case which used middle-of-the-road
forecasts of future variables with a case that includes the RGGI policy. The model
predicted that RGGI CO2 emission allowance prices would remain in the range of $2-$3
per short ton through 2015 (ICF Consulting 2006b, p. 8). Net electricity imports to the
2
http://mis.nyiso.com/public/P-8list.htm
As indicated by the fact that predicted electricity consumption is lower in the RGGI case than in the
business-as-usual case (RGGI 2006b, p. 6).
3
5
RGGI states4 are higher in the RGGI case than in the business-as-usual case, and
cumulative inter-regional emissions leakage is estimated at 27% of net cumulative RGGI
CO2 emission reductions through 2015 (RGGI Inc 2007a). This means that emissions
increases in non-participating states are predicted to increase by 27% of the RGGI
reductions, where the RGGI reductions are net power plant CO2 emission reductions
resulting from RGGI in the RGGI states plus CO2 emission reductions from offsets.
Offsets are extra allowances earned by sponsoring projects that reduce greenhouse gas
emissions from sources other than power plants (RGGI Inc 2007b). If offsets are
excluded from the calculation or RGGI reductions, inter-regional leakage is
approximately 50% or RGGI reductions (ICF Consulting 2006b, p. 12). Because the ICF
estimates of emission reductions may not include some of the demand-side effects of
RGGI on emissions, they may be an incomplete prediction of total emission reductions.
As a result, these ICF leakage predictions may overstate leakage as a percentage of
emission reductions.
The ICF study predicts that most of the leakage will be a result of a long-run supply-side
effect: investors choosing to locate new combined cycle gas-fired generators outside of
RGGI rather than inside of RGGI. The Initial Report of the RGGI Emissions Leakage MultiState Staff Working commented on this prediction, calling it “an outcome that Staff
deems to be unlikely in the real world” (RGGI Inc 2007a).
In addition, the ICF simulation predicts that most of the leakage would occur in nonRGGI PJM states, such as Pennsylvania, rather than in Canadian provinces or in non-PJM
US states. The RGGI Staff Working Group concluded that given the limitations in the
model, and the uncertainty about its investment prediction, “there is insufficient
information to make refined estimates as to the potential amount of emissions leakage
that may occur over the course of the program.”
Cornell-RPI Study
Shawhan, Mitarotonda, Zimmerman, and Taber (2010) estimate the short-run supplyside inter-regional effects of RGGI using a more complex representation of the
transmission system. They assemble and employ a 36-node alternating-current optimalpower-flow model of the power grid in northeastern North America. Their model
predicts that, with the current transmission system and generators, and non-priceresponsive demand, a RGGI allowance price of $3.87 leads to an immediate emission
reduction in RGGI states of 1.6 million metric tonnes per year. This is an estimate of the
4
Henceforth, the term “RGGI states” shall refer to the ten participating states as well as to the District of
Columbia, which is participating.
6
short-run supply-side effect of RGGI on emissions within the RGGI states. Their model
estimates an accompanying CO2 immediate emission increase in surrounding states and
provinces of 1.3 million metric tonnes.5 Therefore, they estimate that the short-run
supply-side effect of RGGI on CO2 emissions is subject to 82% leakage (Shawhan et al.
2010, p. 18). Because this study includes only the short-run supply-side effects of RGGI
on emissions, it predicts only a portion of the total emission reductions from RGGI. As a
percentage of the total RGGI-induced emission reductions, the emission increase in
neighboring states and provinces (i.e. inter-regional leakage) could be expected to be
smaller than 82%, perhaps much smaller. For this same reason, the Shawhan et al.
estimate is not inconsistent with the ICF estimate, since the ICF estimate includes more
components of the total emission reductions that can be expected to result from RGGI.
Since the start of the initial compliance period on January 1, 2009, the price in the
secondary market for RGGI allowances has never been higher than $4.13. In the most
recent auctions, the allowances have sold for $1.86, which is the reserve price. The
figure below shows a history of RGGI allowance prices from one month prior to the date
that the allowance requirement took effect. It reveals that the allowance price
prediction of the ICF study and the allowance price assumption of the Shawhan et al
study have approximately correct magnitudes.
5
This is the same type of leakage that we attempt to detect and measure in the present study. This
estimate of 1.3 million tonnes per year is short-run supply-side leakage in all neighboring states and
provinces, while we attempt to detect only the portion in the important neighboring state of
Pennsylvania.
7
RGGI Auction and Secondary Market Prices
$4.50
$4.00
$3.50
$3.00
$2.50
$2.00
$1.50
$1.00
$0.50
$-
12/1/08
3/11/09
Secondary Market
6/19/09
9/27/09
1/5/10
Current Period Auction
4/15/10
7/24/10
Future Period Auction
11/1/10
Reserve Price
Sources: www.rggi.org and www.ccfe.com
PSEG Study
The Public Service Electric and Gas Company (PSEG) performed a study to determine the
CO2 emissions impact of their proposed Susquehanna-Roseland transmission line
connecting Pennsylvania to New Jersey. The analysis used the production cost modeling
software PROMOD,6 with a detailed security-constrained unit commitment and dispatch
module. The base simulations assumed PJM’s 2013 projection of peak load and
assumed that RGGI would be in effect. This analysis predicted that the SusquehannaRoseland line would, in the base case, decrease emissions in the RGGI region by 218,862
tons and increase emissions outside of RGGI by 353,189 tons, for a net increase of
134,336 tons in PJM, which is 0.03% of the overall CO2 emissions in PJM. A second case
assumed a national CO2 price instead of RGGI in order to isolate the emission effect of
the line not attributable to its interaction with the RGGI policy. This scenario estimated a
net increase of 64,403 tons CO2. Hence, about half of the line’s impact on emissions
would occur in the absence of the RGGI policy. This indicates that about half of the
line’s effect on power transfers from Pennsylvania to New Jersey, and on emissions, in
the base (RGGI) case are due to fuel cost differentials, rather than RGGI compliance
6
http://www.ventyx.com/analytics/promod.asp
8
costs.7 While this analysis only considered the effects of the new transmission line, it
shows that the low-cost coal power in western PJM creates an economic incentive to
transmit more emissions-intensive power from Pennsylvania if the transmission system
will allow it. In the analysis, the change in emissions in each area comes mostly from
combined-cycle gas unit output in RGGI states being replaced with coal unit output in
Pennsylvania. In this case the net emission increase is caused by the removal of
transmission congestion.
Econometric Analysis of RGGI Leakage
For our analysis we empirically test for CO2 emissions leakage from the RGGI states to
Pennsylvania, which is not a member of RGGI. If the ICF study is correct in its prediction
that most of the leakage will occur in non-RGGI PJM states, then it seems likely that
much of it will occur in Pennsylvania, since Pennsylvania is the non-RGGI PJM state in
closest proximity to most of the RGGI states, and has an abundance of coal-fired
generation capacity.
Unlike previous studies on leakage that have used production cost models with
representations of transmission topology to estimate leakage under various scenarios,
this study attempts to directly measure emissions leakage by examining historical data.
An empirical relationship between the RGGI allowance price and either Pennsylvania
emissions or scheduled power imports from Pennsylvania to a RGGI state, controlling for
other factors, would indicate emissions leakage. Our study evaluated scheduled flows
rather than actual flows because schedules should more accurately reflect the economic
evaluation of power costs between regions than actual flows. System operators
dispatch the power system to match actual flows to schedules; but unanticipated events
can cause actual power flows to somewhat deviate from scheduled transactions,
causing random, counter-intuitive flows.
Changes in emissions that result from the low RGGI allowance prices are likely to be
difficult to separate from other factors that influence emissions. One reason is the
magnitude of the RGGI allowance price relative to the price of electricity. If the typical
marginal generation unit in the RGGI states has a marginal emission rate of 0.5 tons per
MWh and the RGGI allowance price is $2 per ton, then the effect on the marginal
generation cost of that marginal unit is $1 per MWh. This is small relative to the
7
Public Service Electric and Gas, in New Jersey BPU Docket EM00010035, Exhibit SRTT-114
9
average wholesale cost of electricity. For example, the average wholesale cost in the
NYISO in 2010 was $58.92. Another reason for the difficulty of detecting statistically
significant effects of the RGGI allowance price is the large variations in other variables
that are likely to also affect net imports to the RGGI states and emissions in neighboring
jurisdictions. From 2005 to 2009, carbon dioxide emissions decreased by 33 percent in
the RGGI region.8 Identified reasons are decreases in load, fuel switching from
petroleum and coal sources to natural gas, and changes in capacity mix from increases
in wind, nuclear, and hydropower. Absent large changes in the emission characteristics
of generators, electrical load is the major determinant of the quantity of carbon dioxide
emissions.
Analysis of Scheduled Flows from Pennsylvania to New York
Our first means to test for emissions leakage is to attempt to detect a statistically
significant effect of RGGI allowance prices on power flows between Pennsylvania and
New York. New York shares seven high-voltage interties with Pennsylvania, which has an
electric supply mix dominated by coal generation and is ranked second in the nation for
CO2 emissions.9 Power can also flow from Pennsylvania to New York through New
Jersey, which is a RGGI state. However, we chose to test our methodology only on flows
that can be directly measured. New York is also interconnected with Ontario and
Quebec. While neither is a member of RGGI, the electric energy in these Provinces is
primarily from non-emitting sources such as hydroelectric and nuclear power
generators. Finally, New York is also interconnected with the other RGGI states of
Massachusetts, Connecticut, and Vermont.
Higher net flows from Pennsylvania to New York associated with higher RGGI allowance
prices would indicate emissions leakage. This assumes that some of the additional
generation in Pennsylvania would produce emissions, but that is virtually certain. In
PJM, the regional system that includes Pennsylvania, a coal-burning unit was the
marginal unit 74% of the time and a gas-burning unit was the marginal unit 22% of the
time (Monitoring Analytics 2010).
8
Relative Effects of Various Factors in RGGI Electricity Sector CO2 Emissions: 2009 Compared to 2005,
Prepared by NYSERDA for RGGI Inc., November 2010
9
http://tonto.eia.doe.gov/state/state_energy_rankings.cfm?keyid=86&orderid=1
10
Data Selection and Description for Scheduled Flows Analysis
We estimate a model that predicts scheduled flow of electric energy (i.e. external
transactions) across the Pennsylvania-New York border, with the RGGI allowance price
as one of the explanatory variables. If the coefficient on the RGGI permit price were
positive and statistically significant, it would be empirical evidence of CO2 emissions
leakage from the RGGI states to Pennsylvania.
A weekly time step is used because of the high variability in daily scheduled flows that
we cannot explain well. There are many cases of 100 percent increases or 50 percent
decreases in flows from one day to the next which may result from influential but
difficult-to-measure events. This is true of both the real-time and day-ahead scheduled
flows.
We try both day-ahead scheduled flow and real-time scheduled flow as our dependent
variable, i.e. as the variable we predict. Both are measures of average scheduled flow
over the AC interfaces during the week in question. The market for real-time scheduled
flow closes 75 minutes in advance of each hour. The first dependent variable is dayahead scheduled flows, which might have more predictable variation than real-time
scheduled flows because it is unaffected by unpredictable events that arise less than a
day in advance. This is the dependent variable in model (1) of Table 2 in the Appendix.
The second dependent variable is real-time scheduled flows.
Flows attributable to wheel-through transactions, which are intended to pass through
rather than sink in a control area, are not included in either of our dependent variables.
In the time period studied, wheel-through transactions constituted only 2% of dayahead and real-time imports from Pennsylvania by volume.
The main drivers of scheduled flows should be load (i.e. quantity of power demanded)
on each side of the Pennsylvania-New York border. New York’s load, when high, may
require New York generation to be supplemented with more imported electricity in
order to satisfy demand at lowest total cost. Pennsylvania’s generators primarily serve
the PJM market, consisting of the states south of New York, whose load may affect the
availability of exports to New York. In our models, the load we use for PJM is
contiguous-zone PJM, which consists of its entire load area except for the
Commonwealth Edison zone, which is in northern Illinois and is not geographically
contiguous with the rest of PJM.
11
Lower hydro and nuclear output in New York would increase the need for imports to
New York, while lower output from such sources in Pennsylvania would decrease the
availability of exports from Pennsylvania. Hydroelectric and nuclear generation from
existing facilities are unlikely to be affected by the RGGI allowance price since
hydropower output is constrained by water availability and nuclear generators operate
at near maximum output at all times, except when undergoing maintenance.
Imports of electricity from New England are included as an explanatory variable because
they may decrease the need for imports from Pennsylvania and because we consider
them unlikely to be affected by the RGGI allowance price, since RGGI applies equally in
New York and New England. Imports from Ontario and Quebec are not included because
they may be influenced (i.e. endogenously determined) by the RGGI price, and therefore
not independent of potentially RGGI-driven imports from Pennsylvania.
Several measures of fuel prices were included to try to determine the correct impact of
such prices on electricity imports from Pennsylvania to New York. A higher ratio of the
regional natural gas price to the monthly average coal price paid by Pennsylvania coalfired generators may make imports more economically viable since coal-fired plants
represent a larger share of generation capacity in Pennsylvania than in New York. There
is also a reason to try natural gas price as the sole fuel-price variable, as our data on the
average price generators paid for coal may not be a good proxy for their marginal cost
of coal.
All power plants in the eastern United States must have allowances to emit nitrogen
oxides (NOX) and sulfur dioxide (SO2). The NOX allowance price may affect imports
because the near-marginal power plants may have higher emission rates in Pennsylvania
than in New York. The square of de-meaned NOX allowance price is included as an
additional explanatory variable due to the appearance of a non-linear relationship in a
graph of imports versus the NOX price. The NOX allowance prices were at $750 at the
beginning of 2008 and dropped over time to $27.5 in late 2010. This is why the standard
deviation of the NOX allowance price is high compared to its average of $418.12. The
effect of SO2 allowance prices was also examined but was highly insignificant.
Results of Leakage Tests in Scheduled Flows Analysis
Model (1) in Table 2 in the Appendix represents the tested models that use day-ahead
scheduled net flows from Pennsylvania to New York as the dependent variable. The test
for CO2 emissions leakage from RGGI is whether the RGGI allowance price is a
12
statistically significant explanatory variable for flow. In this model, the RGGI CO2
allowance price is highly insignificant as an explanatory variable for flow. The coefficient
for the RGGI price is negative, but has a 95% confidence interval of -70.36 to 49.59 MW.
Hence, the analysis provides no evidence that the RGGI price has had an effect on
power imports from Pennsylvania to New York
Like model (1), the non-shown models using day-ahead scheduled flows as the
dependent variable produced a low adjusted coefficient of determination (“r-squared
value”). An r-squared value measures the amount of variation in the dependent variable
that is predicted by the model. The inability of our models to explain more of the
variation in day-ahead scheduled flows led us to also try real-time scheduled flows as
the dependent variable.
In spite of their susceptibility to unforeseen events that arise with less than a day’s
notice, real-time schedules have a lower standard deviation than day-ahead schedules.
Models (2) – (6) use real-time schedules and produce higher r-squared values than
model 1. These r-squared values are close to 0.4. In all six models, the variables with pvalues (“p” in the footnotes of the tables) of less than 0.05 all have the expected signs
for their coefficients.
We now discuss the results of models (2) – (6). For variables included in more than one
of these models, the results are similar across models. Where we need to discuss
individual coefficient estimates or significance tests to represent this set of models, we
discuss those in model six (6).
In models (2) to (6), as in model (1), the test for CO2 emissions leakage is whether the
RGGI allowance price is a statistically significant explanatory variable for flow. Table 2
shows that the estimated coefficient associated with the RGGI CO2 allowance price is
negative. A negative coefficient, if true, would indicate that a higher RGGI allowance
price, and the existence of RGGI, reduces imports from Pennsylvania to New York, and
produces negative short-run leakage of CO2 emissions across this border. We are not
aware of any likely reason for this to occur, and the estimated negative coefficient is not
statistically significant at the 95 percent level in any of the models.
Other Results from Scheduled Flows Analysis
We now discuss the other results of the scheduled flows models, aside from their tests
for leakage. A one-MW increase in New York state’s average load increases average net
13
import flow by an estimated 0.124 MW. A one-MW increase in PJM’s average load
decreases average net import schedules to New York by an estimated 0.04 MWh. Both
of these results are fairly consistent across the other four real-time scheduled flows
models (models 2-5) and the day-ahead scheduled flows model (model 1).
The models all indicate a relative unimportance of the average price paid for coal in
explaining variation in scheduled flows. The Pennsylvania and New York coal prices are
both insignificant when included, together or separately, in a model with the natural gas
price as another explanatory variable. The coal prices are also highly insignificant even in
the absence of the natural gas price, as indicated by models not shown in Table 2. This is
likely due to the fact that most coal is purchased under long-term contracts, and
therefore most coal plants are insensitive to spot fuel price variations relative to natural
gas generators. The natural gas price is significant by itself in models (2), (5), and (6) and
the gas to coal price ratio is significant in models (3) and (4). However, this seems to be
primarily a result of the importance of natural gas price variation in the ratio. The coal
price has a standard deviation of only 30 cents, indicating that it is relatively stable
around its mean of $2.33 per million British thermal units (MMBTU). On the other hand
the natural gas price is relatively more variable around its mean of $6.75 per MMBTU, as
indicated by its standard deviation of $3.11. Therefore, changes in the ratio are mostly
because of changes in the natural gas price. The New York natural gas price has a large
effect on real-time scheduled imports. A one dollar increase in the natural gas price per
MMBTU is associated with an increase in average hourly real-time imports of 84.354
MW.
The NOX emission allowance price also appears to have a large effect. The total
estimated effect is the sum of the effects of NOX allowance price and the square of NOX
allowance price. According to this total estimated effect, imports decline with higher
NOX prices over the range of prices observed in the time period of our observations, but
the estimated marginal effect of higher NOX prices is close to zero at the high end of the
observed NOX price range.
New York hydroelectric generation has a p-value close to 0.05 in the various models,
while New York nuclear generation was dropped in model six (6) due to statistical
insignificance. Pennsylvania nuclear generation is significant at the five percent level
(i.e. p < 0.05) and a one MW increase in its output increases average hourly real-time
scheduled flows by an estimated 0.109 MW.
14
Actual and Predicted Imports from Pennsylvania to New York
2,500
MW
2,000
1,500
1,000
500
0
2008
2009
Actual RT Schedules
2010
Predicted from Regression
Despite using weekly data in an attempt to reduce the unexplained variation found in
the daily data, there are still large differences in week to week imports which our data
and model do not explain, as indicated by the variation of the actual weekly average
scheduled flow (green line) around the values our model predicts (red line). This is
consistent with an observation by the New York Independent Market Monitor in his
2009 State of the Market Report (p. 59). In it, he comments on the failure of electricity
prices to be fully arbitraged across the border:
•
•
•
First, market participants do not operate with perfect foresight of future
market conditions at the time that transaction bids must be submitted.
Without explicit coordination between the markets by the ISOs, complete
arbitrage will not be possible.
Second, differences in scheduling procedures and timing in the markets
serve as barriers to full arbitrage.
Third, there are transaction costs associated with scheduling imports and
exports that diminish the returns from arbitrage.
For transaction flows to equalize prices on both sides of the border, and perhaps be
more thoroughly explained by fundamental factors such as load on each side of the
border, harmonization of rules and centralized dispatch would be required.
15
Analysis of Pennsylvania CO2 Emissions
Our second means for attempting to detect leakage is to test for a statistically significant
effect of the RGGI allowance price on daily CO2 emissions from Pennsylvania’s electric
power sector. Most of the variables are the same for the Pennsylvania CO2 emissions
model as they were in the flows models, except that we use the daily values of these
variables rather than the weekly values. The main difference is in the coal prices. The
coal prices we use in the analysis of Pennsylvania emissions are reported spot market
prices, due to their daily availability.
Temperature is included in this model because of its potential impact on the efficiency
of thermal generation and hence on emissions. Coal and natural gas generators have
reduced production on days with higher temperatures because their generation
depends on the temperature difference between the combustion chamber and the
ambient air. Separately, a dummy variable for work days (“on peak days”) is used
because of the different markets for “peak” (16x5) and daily (24x7) power transactions.
Results of the Pennsylvania Emissions Analysis
The regression results of our Pennsylvania CO2 emissions models are presented in Table
4 of Appendix 1. With the exception of the binary “On Peak Day” variable, the variables
are all standardized in order to make interpretation across the various units of
measurement easier. Each value indicates how much of the variation of the dependent
variable is explained by the variation of the explanatory variable corresponding to that
row. All six models have adjusted r-squared values of between 0.7960 and 0.7967
regardless of model specification. In a large part this is because of the dominant impact
of load on emissions. These adjusted r-squared values are much higher than those in the
models with day-ahead or real-time scheduled flow as the dependent variable,
suggesting that the models with Pennsylvania CO2 emissions as the dependent variable
may offer a better chance of determining the true impact of the RGGI CO2 allowance
price. The graph below compares the actual daily Pennsylvania CO2 emissions to those
predicted by our model (6).
16
Actual and Predicted Pennsylvania CO2 Emissions
500
450
Tons (,000)
400
350
300
250
200
150
1/1/2008
5/1/2008
9/1/2008
1/1/2009
Actual CO2
5/1/2009
9/1/2009
1/1/2010
5/1/2010
9/1/2010
Predicted from Regression
In all six of the models, the p-value for RGGI allowance price is approximately 0.6,
indicating that the effect of RGGI on Pennsylvania CO2 emissions is statistically
insignificant. The RGGI allowance price has a positive, very small coefficient in the first
four models, and a negative, very small coefficient in the last two models. The 95
percent confidence interval for the RGGI price in model (6) is -0.104 to 0.077. We
conclude from this that our analysis of Pennsylvania CO2 emissions provides no
evidence of leakage.
We now consider the other results of this analysis. Again, the estimated coefficient
values and significant test results are similar across the six models. Estimated
coefficients with p-values less than 0.05 have the expected signs. In model (6), a one
standard deviation increase in nuclear output, 726.89 MW, from Pennsylvania nuclear
generation decreases total daily Pennsylvania CO2 emissions by an average of 334 tons.
Several variables hypothesized to affect Pennsylvania CO2 emissions are statistically
insignificant. The SO2 allowance price and temperature were dropped after model (1) as
they were highly insignificant in that model. Removing them from the models has only
minimal impacts on other coefficients and the adjusted r-squared value. Pennsylvania
17
natural gas and coal prices were insignificant when both were used together, when the
ratio was used, and when each was used without the other. This indicates that there is
no statistically significant, detectable change in emissions resulting from variation in fuel
prices. A potential reason for this is that most CO2 emissions in Pennsylvania come from
coal generation, which is used as base loaded generation in Pennsylvania. It may be
that the natural gas price never reached a low enough level, relative to the coal price, to
have a sufficiently large effect on emissions. In New York, the effects of gas prices may
be more significant, where owners of several coal-fired generators have announced
financial losses and retirements.10
10
AES Eastern Energy, New York, recorded an impairment charge of $827 million, citing a decline in
power prices relative to the price of coal. Source: AES Company Release 2/28/2011.
18
Conclusion
Our econometric analysis does not support the hypothesis that RGGI compliance costs
have caused emissions leakage. It appears that the RGGI price is too low to permit the
empirical detection of inter-regional emissions leakage, at least using the models and
data we have employed. We attempted to detect a statistically significant effect of the
cost of RGGI compliance on power flows from Pennsylvania into New York or on CO2
emissions in Pennsylvania, and we found neither.
Further research could be performed on the presence and extent of emissions leakage
from RGGI. First, one could repeat the analysis described above in the future, once more
data is available. Second, instead of analyzing only leakage to Pennsylvania leakage, one
could include a larger set of states and provinces bordering on the RGGI states. Third,
leakage from RGGI is not limited to interregional electricity trade. Since generators with
capacities of 25 MW or less in RGGI states are exempted from holding RGGI CO2
permits, leakage to them is also likely. One could empirically estimate this leakage using
historical dispatch and emissions data from these generators. Fourth, one could
attempt to measure long-term supply-side leakage by estimating the determinants of
investors’ decisions about where to build power plants. The RGGI policy would be one
of these determinants.
The larger picture regarding CO2 emissions in New York and Pennsylvania is that they
have fallen since the RGGI agreement came into effect in 2009.11 Analysis by NYSERDA
of factors explaining this change in CO2 emissions concludes that recent decreases are
being primarily driven by the trends of decreased demand, cleaner fuels, and more
efficient generation. While much of the demand reduction for electricity in recent years
can be attributed to the weak economy, and is therefore reversible, the other trends are
secular and should sustain reductions in CO2 emissions. These trends include
government and private investment in energy efficiency, advancements in drilling
technology unlocking vast supplies of unconventional natural gas, and the continual
replacement of steam boiler technology with more efficient combustion turbines paired
with heat-recovery steam generators.
11
Relative Effects of Various Factors in RGGI Electricity Sector CO2 Emissions: 2009 Compared to 2005,
Prepared by NYSERDA for RGGI Inc., November 2010
19
Appendix 1 – Model Variables and Results
Table 1: Summary Statistics for Variables in Regression of Scheduled Flows from Pennsylvania to New York
Variable
1
Day-Ahead Scheduled
2
Flows
Real-time Scheduled
2
Flows
NY Average Load
PJM Contiguous Average
Load
NY Nuclear Output
NY Hydro Output
PA Nuclear Output
Electricity Imports from
New England
RGGI CO2 Allowance Price
NY Natural Gas Price
NY Average Price Paid for
Coal
PA Average Price Paid for
Coal
Ratio of NY Natural Gas
Price to PA Average Price
Paid for Coal
Ratio of NY Price Paid for
Coal to PA Average Price
Paid for Coal
NOx Allowance Price
Definition
Average hourly imports from Pennsylvania across
the PJM Keystone Proxy Bus to New York
scheduled in the day-ahead market
Average hourly imports from Pennsylvania across
the PJM Keystone Proxy Bus to New York
scheduled one hour ahead
Units
Average
Standard
Deviation
MW
1339.28
292.44
MW
1133.79
265.81
MW
18539.42
1996.43
MW
68240.1
7763.54
MW
4911.04
564.03
MW
2811.57
265.81
MW
8776.90
687.22
MW
833.04
286.75
$
2.63
0.68
$/MMBtu
6.75
3.11
$/MMBtu
2.62
0.35
$/MMBtu
2.33
0.18
Ratio of the New York natural gas spot price to
the average price paid for coal in Pennsylvania
41.56
16.09
Ratio of the average price paid for coal in New
York to the average price paid for coal in
Pennsylvania
1.13
0.12
396.14
348.52
Average hourly load in New York
Average hourly load in what we call “contiguouszone PJM,” which excludes the portion of PJM in
Illinois served by Commonwealth Edison
Average hourly nuclear generation output from
all nuclear power plants in New York
Average hourly hydro electric generation output
from all nuclear power plants in New York
Average hourly nuclear generation output from
all nuclear power plants in Pennsylvania
Total daily scheduled imports from New England
in the day-ahead market
Daily price of RGGI CO2 allowance prices (0 until
st
compliance is required, January 1 , 2009).
Average and Standard Deviation to the right
pertain to the time period of 1/1/2009 –
9/30/2010
Price per MMBtu of NY natural gas from TranscoZone 6
Average price paid by generators for coal in New
York. SNL monthly data, interpolated from middle
of each month.
Average price paid by generators for coal in
Pennsylvania. SNL monthly data, interpolated
from middle of each month.
Price of NOx emission allowances
$
Number of Observations: 153
1
All variables are averaged over the week from daily or hourly data
2
Dependent Variable
Data supplied from SNL, the NYISO, the EPA, and the EIA
20
Table 2: Regression of Scheduled Flows from Penn. to New York with Cochrane-Orcutt Transformation
1
Regressor
(1)
NY Average Hourly Load
PJM Average Hourly Load
NY Nuclear Output
NY Hydro Output
PA Nuclear Output
Electricity Imports from New
England
RGGI CO2 Allowance Price
NY Natural Gas Price per MMBtu
0.113***
(0.027)
-0.035***
(0.007)
-0.070
(0.042)
-0.226*
(0.095)
0.108**
(0.036)
-0.249**
(0.089)
-10.382
(30.34)
59.164***
(14.85)
NY Average Price Paid for Coal
PA Average Price Paid for Coal
Ratio of NY Natural Gas Price to
PA Average Price Paid for Coal
Ratio of NY Price Paid for Coal to
PA Average Price Paid for Coal
NOx Allowance Price
2
NOx Allowance Price
Intercept
Adjusted R
N
2
2
2
2
2
2
(2)
(3)
(4)
(5)
(6)
0.114***
(0.029)
-0.036***
(0.008)
-0.039
(0.044)
-0.187
(0.107)
0.106**
(0.039)
-0.105
(0.096)
-43.506
(29.61)
71.951***
(18.16)
-26.013
(174.67)
-178.12
(215.89)
0.101***
(0.027)
-0.032***
(0.007)
-0.040
(0.044)
-0.208*
(0.104)
0.108**
(0.039)
-0.090
(0.095)
-46.924
(31.13)
0.099***
(0.026)
-0.032***
(0.007)
-0.038
(0.044)
-0.195*
(0.098)
0.103**
(0.038)
-0.085
(0.094)
-46.769
(31.32)
0.125***
(0.027)
-0.039***
(0.008)
-0.044
(0.042)
-0.159
(0.093)
0.106**
(0.037)
-0.131
(0.092)
-46.834
(28.75)
82.031***
(14.66)
0.124***
(0.027)
-0.040***
(0.008)
13.415***
(2.70)
-0.434***
(0.125)
0.0007*
(0.00030)
964.863*
(461.77)
-0.541***
(0.119)
0.0007*
(0.00028)
983.214*
(435.79)
-0.556***
(0.118)
0.0007*
(0.00028)
845.102*
(416.45)
0.3212
152
0.3718
152
0.3781
152
-0.250**
(0.128)
0.0007*
(0.00029)
1517.031***
(443.87)
-0.513***
(0.149)
0.0007*
(0.00031)
1535.375*
(722.09)
13.397***
(2.69)
-154.99
(393.24)
-0.475**
(0.159)
0.0007*
(0.00032)
1135.391
(651.04)
0.2906
152
0.3557
152
0.3219
152
-0.149
(0.092)
0.109**
(0.036)
-0.109
(0.090)
-45.914
(28.46)
84.354***
(14.49)
Rounded standard errors are listed in parentheses under the coefficients
* p<0.05, ** p<0.01, *** p<0.001
1
Day-ahead Scheduled Imports
2
Real-time Scheduled Imports
21
Table 3: Summary Statistics for Variables in Regression of PA CO2 Emissions
Variable
Definition
PA CO2
1
Emissions
PJM Average
Load
Daily CO2 emissions from all electricity generating power plants
in Pennsylvania
Average hourly load from “contiguous-zone PJM,” which
consists of all PJM service areas except Commonwealth Edison
in Illinois
Average hourly nuclear generation output from all nuclear
power plants in Pennsylvania
Daily price of NOx allowance prices
PA Nuclear
Output
NOx Emissions
Allowances Price
SO2 Emissions
Allowances Price
RGGI CO2
Allowance Price
PA Natural Gas
Price
PA Coal Price
PA Gas to Coal
Price Ratio
Temperature
On Peak Days
Daily price of SO2 emissions allowance prices
Daily price of RGGI CO2 allowance prices (0 until compliance is
st
required, January 1 , 2009). Average and Standard Deviation to
the right pertain to the time period of 1/1/2009 – 9/30/2010
Daily spot price of natural gas from Tetco M-3 hub
Daily price of coal from Central Appalachia Big Sandy River
Barge Prompt
Ratio of cost per MWh from natural gas to cost per MWh from
coal. We assume a heat rate of 8,800 Btu/kWh for gas and
11,000 for coal. We use the gas and coal prices in the above
rows, but add $7 per ton to the coal price for transportation.
Daily average temperature, measured in Scranton, PA
1 For “On Peak” days, 0 for “Off Peak” days. On Peak days refer
to all weekdays except for designated Holidays as determined
by the NYISO
Unit
Average
Standard
Deviation
1000 Tons
327.89
51.62
MW
68703.98
8989.7
MW
8774.07
726.89
$
418.12
347.32
$
137.61
139.56
$
2.63
0.68
$/MMBtu
6.68
3.02
S/Ton
67.86
23.87
1.59
0.49
Degrees F
51.09
17.79
Dummy
Variable
0.70
0.46
Number of Observations: 1004
Time Step: Daily 1/1/2008 – 9/30/2010
1
Dependent Variable
Data supplied from SNL, the NYISO, the EPA, and the EIA
22
Table 4: Regression of PA CO2 emissions with Cochrane-Orcutt
transformation
Regressor
(1)
(2)
(3)
(4)
(5)
(6)
PJM Average Load
1.224***
(0.123)
-0.455***
(0.119)
0.00814
(0.0538)
-0.0464*
(0.0198)
-0.130
(0.0686)
-0.0492
(0.0622)
0.0248
(0.0316)
0.0752
(0.0623)
1.220***
(0.123)
-0.453***
(0.119)
0.00897
(0.0540)
-0.0467*
(0.0190)
-0.132
(0.0689)
-0.0525
(0.0623)
0.0251
(0.0316)
0.0794
(0.0626)
1.215***
(0.123)
-0.447***
(0.119)
0.0238
(0.0515)
-0.0465*
(0.0198)
-0.159**
(0.0602)
1.207***
(0.122)
-0.435***
(0.117)
0.0197
(0.0510)
-0.0467*
(0.0198)
-0.154**
(0.0609)
1.213***
(0.122)
-0.446***
(0.118)
-0.0113
(0.0466)
-0.0455*
(0.0198)
-0.108*
(0.0535)
1.206***
(0.122)
-0.434***
(0.117)
-0.0135
(0.0462)
-0.0456*
(0.0198)
-0.106*
(0.0529)
0.0204
(0.0310)
0.0873
(0.0621)
0.0956
(0.0609)
PJM Average Load2
RGGI CO2 Allowance Price
PA Nuclear Output
NOx Price
SO2 Price
PA Natural Gas Price
PA Coal Price
Natural Gas to Coal Price
Ratio
Temperature
On Peak Day
Intercept
0.0125
(0.0189)
0.0128
(0.0198)
0.104***
(0.0191)
-0.104*
(0.0482)
0.104***
(0.0190)
-0.104*
(0.0486)
0.105***
(0.0190)
-0.105*
(0.0491)
Adjusted R2
.7964
.7965
.7965
N
1002
1002
1002
Rounded standard errors are listed in parentheses under the coefficients.
* p<0.05, ** p<0.01, *** p<0.001
0.103***
(0.0188)
-0.103*
(0.0486)
0.105***
(0.0190)
-0.108
(0.0509)
0.102***
(0.0188)
-0.106*
(0.0503)
.7967
1002
.7960
1002
.7962
1002
23
Appendix 2 – Additional Detail About Methods
Models were estimated in STATA, an advanced statistical analysis package. Explanatory
variables, to be used to attempt to explain the dependent variables of Pennsylvania CO2
emissions and Pennsylvania-to-New York power flows, were selected based on previous
studies, first principles, data availability, experience of the authors and input from
others at the NYISO.
Results from initial ordinary least squares (OLS) regression analysis indicated strong
serial correlation. This occurs when the prediction error in consecutive time periods are
correlated. The presence of serial correlation was determined by graphs of the residuals
over time which indicated positive serial correlation and by statistical tests. DurbinWatson D-statistics were approximately 0.6. Breusch-Godfrey tests for first-order serial
correlation were statistically significant. Partial autocorrelograms of the residuals
indicated first order correlation as the observations in the first time period were highly
significant followed by insignificance in later time periods.
Initial regressions were analyzed for other structural defects. Cook and Weisberg tests
for heteroscedasticity were insignificant, indicating no heteroscedasticity problems. A
Phillips-Perron unit root test was performed to test for stationarity of the dependent
variables. Results were significant, leading to a rejection of the null hypothesis of a unit
root, and support for the alternative hypothesis that the dependent variables could be
treated as stationary during the timespan of our data.
To correct for first order serial correlation, models were estimated using generalized
least squares (GLS) methods. The Cochrane-Orcutt transformation was used to
iteratively estimate the coefficient of first order serial correlation, ρ (“rho”). The
estimated ρ is the value of the regression coefficient that would result from a regression
of each error term on the error term from the preceding time period. Since the exact
value of this regression coefficient is unkown, the Cochrane-Orcutt transformation
iteratively estimates its value and then corrects for the serial correlation using the
estimated ρ.
Results of the transformed model were tested to determine the effectiveness of the
correction. Durbin-Watson D-statistics were close to two indicating little or no
remaining serial correlation. Breusch-Godfrey tests for first order serial correlation were
insignificant. Finally, the error term from the estimate of the dependent variable was
put into an AR(1) ARIMA model in which it was regressed against the lag of itself. A
24
Portmanteau white noise test failed to reject the hypothesis that the resulting residuals
were white noise indicating that serial correlation was corrected.
Multicollinearity was determined not to be a problem. The variance inflation factors
(VIF) for all explanatory variables were under 5 in each model, except in the models of
scheduled flows, in which New York natural gas price had a VIF just above 7. GLS
regressions run on the New York natural gas price with individual explanatory variables
result in no relationships with p-values below 0.2.
Visual inspection of the data indicated non-linear relationships between scheduled flow
and the NOx allowance price, and between Pennsylvania CO2 emissions and PJM load.
In each case, including the square of the explanatory variable in question as an
additional explanatory variable produced a higher adjusted r-squared than omitting the
square or including both the square and the cube.
Relationships of each dependent variable with other explanatory variables were also
tested by trying non-linear transformations of the explanatory variables such as
squaring, logging, and linear or cubic splines. For the variables other than the two
mentioned in the preceding paragraph, linear relationships were judged suitable, as
determined by changes in adjusted r-squared values, p-values of the variables, and Ftests for joint significance.
25
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