Robert M.
La Follette School of Public Affairs
at the University of Wisconsin-Madison
Working Paper Series
La Follette School Working Paper No. 2013-007
http://www.lafollette.wisc.edu/publications/workingpapers
Credibility, ambition, and discretion
in long-term U.S. energy policy targets from 1973 to 2011
Gregory F. Nemet
Assistant Professor, La Follette School of Public Affairs at the University of Wisconsin-Madison
gnemet@lafollette.wisc.edu
Peter Braden
La Follette School of Public Affairs at the University of Wisconsin-Madison
Edward Cubero
La Follette School of Public Affairs at the University of Wisconsin-Madison
Bickey Rimal
La Follette School of Public Affairs at the University of Wisconsin-Madison
May 2013
1225 Observatory Drive, Madison, Wisconsin 53706
608-262-3581 / www.lafollette.wisc.edu
The La Follette School takes no stand on policy issues; opinions expressed
in this paper reflect the views of individual researchers and authors.
Credibility, ambition, and discretion in long-term
U.S. energy policy targets from 1973 to 2011
Gregory F. Nemet, Peter Braden, Edward Cubero, and Bickey Rimal
La Follette School of Public Affairs
University of Wisconsin–Madison
nemet@wisc.edu
May 8, 2013
Abstract
A look at the past 40 years of U.S. energy policy provides ample evidence of volatility, including rapidly changing budgets, moving targets,
and shifting incentives. Changing policy too often is a serious criticism
because systemic inertia—for example due to the long-lifetimes of capital stock and to the atmospheric residence time of CO2 —implies a
need for persistence in order to achieve social goals. Further, a pattern of failing to meet objectives may reduce the credibility of future
targets, and thus reduce the incentives for investment and behavioral
change. But changing policy has benefits as well: it allows for adaptive management, experimentation, policy learning, and assimilation
of new information. This paper reviews the effectiveness, duration,
and ambition of 63 energy policy initiatives with targets ≥5 years. We
find: targets were met 64–77% of the time; median duration to target was 12 years; and median rate of change was 2%/year. Significant
predictors of success in meeting targets are enforcement, duration, and
ambition. These determinants are robust across multiple specifications
and definitions of ambition and success. We find a significant decline
in ambition over time. Binding targets are much more likely to be met
than non-binding ones, but discretionary clauses completely offset the
effect of enforceable penalties on the likelihood of target attainment.
keywords: energy policy, credibility, targets, ambition, discretion.
1
1
Introduction
The characteristics of both the energy system and the problems associated
with it imply that energy policy making should take a long term perspective. Consider for example, the 50–80 year lifetime of capital stock in the
energy system or the nearly century-long residence time of CO2 in the atmosphere. As a consequence of these time scales, energy system modelers
often employ longer time horizons, speaking in terms of 2050 as an ‘intermediate’ period, often using 2100 as an endpoint, and even conducting serious
analyses of outcomes in 2200. Policy making typically operates with much
shorter time scales, reflecting election frequency, business cycles, and shifts
in social priorities. Despite the perpetual presence of near term imperatives,
policy makers have made several attempts to design energy policy that is
not short-term focused, but rather, more commensurate with decadal time
scales inherent to energy problems. This study assesses two questions: To
what extent have we been successful in meeting the longer-term targets we
have set? What characteristics of policy initiatives are associated targets
that have been met? More broadly this study seeks to provide a basis for
subsequent work and provide an initial basis on which to address the question does the frequency of goal attainment effect the credibility of subsequent
proposals?
We looked at the ex ante expectations, performance to goal, and repercussions of 63 energy targets involving targets of 5 years or greater. In this
paper, we first discuss in section 2 the tradeoffs between setting long term
targets and maintaining discretion among policy leaders. In section 3 we discuss our selection of policies and approach to evaluating them.1 Section 4
1
Further details about the approach and data used are included in a Supporting Infor-
2
presents the results of our efforts to identify predictors of successfully meeting targets. Finally, in section 5 we discuss implications of the past 40 years
of long term commitments, and particularly their current relevance–both for
policy design and resulting incentives.
2
Flexibility and long-term commitments
For several reasons, governments are intimately involved in decisions related to energy production. Foremost, multiple externalities—air pollution,
climate change, security concerns associated with the maintenance of reliable energy supplies, and macro-economic disruption associated with sudden
changes in prices—provide justification for a government role in influencing
private decisions on energy. Knowledge externalities require government
support for energy technology development. Indeed the recognition of pervasive social externalities from energy during the 1960s and early-1970s was
a primary motivation for the development of a new institution, the Federal Energy Administration in 1974, which later became part of the broader
Department of Energy (Regens and Rycroft, 1981). Further, energy consumption directly accounts for roughly 10% of GDP and is an intermediate
input for the production of almost every good and service in the economy.
The transition to an energy system that can improve access to energy services while reducing the social costs of its externalities—even if primarily
implemented by the private sector—requires government action to provide
adequate incentives .
mation (SI) document: https://mywebspace.wisc.edu/nemet/web/si_targets.html.
3
2.1
Implications of long lifetimes in the energy system
The characteristics of the energy system, and its associated impacts, imply
that at least some government actions need to assume a long-term perspective. First, the long life-time of capital stock in the energy sector leads to
lengthy technology substitution times, on the order of decades (Grubler,
1991; Knapp, 1999; Comin and Hobijn, 2010). Power plants, pipelines,
transmissions systems, buildings, and roads are built to last for 50–80 years.
Second, due to pervasive economies of scale in the energy system (Wilson,
2012), such investments often involve billions of dollars and are essentially
binary decisions, rather than continuous choices that can be tuned to changing conditions. Third, some energy problems have inherent lags. For example, the residence time of greenhouse gases in the atmosphere is on same
multi-decadal time scale as infrastructure transitions. CO2 emitted today
will continue to reradiate heat for close to a century regardless of subsequent efforts at mitigation. As a result, transitions in the energy system
often involve large investments that can take decades to payoff. Decisions
about, whether to build a new power plant, what type of plant to build,
whether to invest in pollution controls, or new transmission capacity reflect
expectations about conditions many years in the future.
2.2
Credibility of commitments
Because of the central role of government in providing incentives, payoffs to
many investments depend on the states of policies several years, to decades,
in the future (Nemet, 2009; Gallagher et al., 2012). Historically, energy
policies have been notoriously volatile (Nemet, 2010b). If investors view
historical policy volatility as an indicator of future policies, they will be
4
skeptical of the longevity of energy policies that involve long-term, and even
medium-term, targets (Helm et al., 2003). For incentives to be effective they
need to convey reasonably certain expectations of persistence.
Previous work on energy shows that investment and social outcomes are
highly sensitive to perceptions of the credibility of future policy commitments. For example, Teisberg (1993) concludes that regulatory uncertainty
leads utilities to delay their investments and choose smaller and shorter-lead
time plants. Bosetti and Victor (2011) find that lack of regulatory credibility results in the most significant increase in costs when compared to the
ideally regulated baseline. Kettunen et al. (2011) find that carbon policy
uncertainty may lead to a more concentrated and less competitive market
structure because larger firms are less risk averse and can borrow money at
more favorable terms than new entrants. Delmas and Heiman (2001) assert
that the relative failure of the American nuclear industry when compared
to the French experience is primarily due to the lack of institutional commitment in the U.S. Other work has found similar results for independent
power producers in the U.S. (Ishii and Yan, 2004), hydropower investment
in Quebec (Saphores et al., 2004), and low-carbon electricity in Australia
(Reedman et al., 2006). A particularly rich, and relevant, area of work
involves the assessment of incentives on investment in carbon capture and
sequestration under carbon price uncertainty (Blyth et al., 2007; Reinelt and
Keith, 2007; von Stechow et al., 2011). Policy discretion plays a particular
role; looking at investment in renewables in the Ontario and Texas, Holburn
(2012) finds that less flexible policy making processes, with agencies that
are relatively independent of elected politicians, are associated with reduced
regulatory risk.
5
2.3
The case for flexibility
Despite the apparent need for long term commitments, there are several
benefits to incorporating flexibility within a broader commitment. First,
flexibility allows for policy experimentation. One can intentionally implement various instruments for shorter periods and iteratively adjust policy
design to seek better outcomes. Second, flexibility can allow one to recover
from mistakes or policy failures (Aldy et al., 2003). A recent example is
the first period of the european union’s CO2 emissions trading system (EU
ETS) (Neuhoff, 2011). Third, it that allows decision makers to make use
of new information and pursue adaptive management (McLain and Lee,
1996). Fourth, flexible policies can take into account changes in social priorities, precipitated for example by price shocks, recessions, armed conflicts,
financial crises, and natural disasters. Finally, policy discretion may allay
concerns in controversial legislation.
2.4
Navigating a tradeoff
Other areas have addressed this tradeoff between the benefits of commitment
and those of flexibility, most notably in monetary policy (Lohmann, 1992),
the finance industry (Nosal and Ordonez, 2013), and even in the early years
of the U.S., when lack of credibility in the new dollar contributed to its
collapse (Grubb, 2011). An important attribute of this time-inconsistency
problem is that participants’ expectations are dynamic; they adjust their
expectations if they know that policymakers have discretion (Kydland and
Prescott, 1977). Assuming dynamic expectations favors commitments with
limited flexibility. However, in some cases, flexibility in the form of policymakers’ discretion can enhance credibility by making the policy making
6
process more robust to major changes (Cowen et al., 2000). Carlson and
Fri (2013) have recently brought attention to this tradeoff in energy policy
design with a call for policy that is both “durable” and “adaptable.” Our
study aims to make an initial empirical contribution to this aspect of energy
policy design.
3
Approach
This study uses data on previous energy policy targets to address two research questions:
1. Have long-term energy policy targets achieved their goals?
2. What characteristics of these targets affect goal attainment?
3.1
Selection of targets and evaluation strategy
We include in our sample federal and state policy targets in the U.S. announced between 1970 and 2011, that addressed energy issues, and that
involve a commitment to a specific quantity at least 5 years after the announcement of the target. This definition excludes local, non-quantified,
and nearer-term goals, as well as policies outside the U.S. For comparison
we do include some high-profile non-U.S. targets, but do not attempt to
be comprehensive in this larger domain. Our selection criteria exclude some
targets that do not fit our comparative framework easily, but probably merit
separate investigation, e.g. the creation of the strategic petroleum reserve
(Blumstein and Komor, 1996), the adoption of a 2 degree climate target
(Randalls, 2010) outside the U.S., and targets subsumed under broader targets that we do include, such as cellulosic contribution to the EISA biofuels
7
Table 1: Targets by geography and policy initiative
Federal
(12) Project Independence
Corporate Average Fuel Economy (CAFE) and
New CAFE
Synfuel I and II
Carter Speech
U.S. Clean Air Act Amendments SO2 I and II
Regional Clean Air Initiatives Market
Energy Policy Act of 2005
Energy Independence and Security Act
Obama Energy Security
State
(21) Renewable Portfolio Standards
State
(24) Energy Efficiency Resource Standards
Non-U.S. (6) Japan New Sunshine
German Renewable Energy Sources Act I and II
EU ETS Phase I, II, and III
targets. We arrived at a list of 63 such targets (Table 1), for which we
provide summaries and references in the SI.
We construct variables to measure characteristics for each of the 63 targets. We use descriptive analysis of the data for these variables to answer
research question 1. To address research question 2, we specify models to
identify the effects of various characteristics on the likelihood of target being
attained. Finally, we conduct a second set of regression in which we identify
the predictors of a target’s ambition, one of the primary variables.
3.2
Characteristics of each target
We code each target for several characteristics, which our literature review
suggest might have an effect on whether a target was met. We use these characteristics to construct variables, which are consistent across all 63 targets.
Where variable construction requires weights or subjective interpretation,
8
we define them in multiple ways and then use all possibly construction to
check the robustness of our regression results. Table 2 summarizes the variables used and the SI provides complete descriptions of how each target was
coded and how each variable was constructed.
Variable
Met v1
Met v2
Binding
Discretionary
Start Year
Duration
RPS
Revised
Ambition v1
Ambition v2
Ambition v3
Ambition Rate
Table 2: Variable Definitions
Definition
1 = target met, in compliance all years, or met in last
year, 0 = target not met or not in compliance any
year,
1 = target met or in compliance all years, 0 = target
not met or not in compliance any year
Does the policy have a binding commitment?
Does the policy have a non-binding commitment?
The year the policy is announced
Years from the start year to the target year
Is the policy a state Renewable Portfolio Standard?
Has the target has been revised since the policy went
into effect?
Sum of the following (weights in parenthesis): accelerate a trend (1=yes, 0=no), reverse a trend (1=yes,
0=no), novel type (1=yes, 0=no), % Change >
Median-all (1=yes, 0=no), % Change > Median-all
(1=yes, 0=no)
Sum of the following (weights in parenthesis): accelerate a trend (1=yes, 0=no), reverse a trend
(2=yes, 0=no), novel type (0.5=yes, 0=no), % Change
> Median-category (1=yes, 0=no), % Change >
Median-category (2=yes, 0=no)
Sum of the following (weights in parenthesis): accelerate a trend (1=yes, 0=no), reverse a trend
(1=yes, 0=no), novel type (1=yes, 0=no), % Change
> Median-category (1=yes, 0=no), % Change >
Median-category (1=yes, 0=no)
Average percentage change required by target.
9
3.2.1
Target Attainment
Measures of whether each target was achieved provide binary dependent
variables to evaluate question 2. We initially code each target to one of
6 categories: met in target year, not met in target year—and in cases for
which target year is after 2012—met in all interim years, met in latest year,
not met in any year, or too early to evaluate. We then create two binary
‘met’ variables that aggregate these 6 categories. Met v1 considers a target
met=1 if the target was attained, if the target has been in compliance every
interim year, or if the targets were met in the latest year. We also use a
stricter version, Met v2, which we coded as 1 if the policys target was met
or in compliance all years. Many observations are dropped for this version.
The SI provides complete descriptions of how each target was coded.
3.2.2
Enforcement
Based on the previous work described above, we expect that targets with
enforcement mechanisms are more likely to be met. We used public records
to qualitatively describe the repercussions of missing a target, e.g. fines and
penalties. If a target include such a mechanism we code it as binding=1.
Among these, some policies include flexibility mechanisms that allow entities
to not meet a target without penalty Nemet (2010a). We code these as
discretionary=1. Examples of these are included in the SI.
3.2.3
Ambition
We expect that more ambitious targets will be less likely to be met. The
challenge here is to characterize ambition in a way that allows direct comparisons across quite heterogeneous types of targets. Our approach is to
10
measure ambition in several ways, construct variables that combine these
measures using different weights, and assess the robustness of the results
across these constructed variables. First, we code the ambition of each
target in 7 ways: (1) did the target reverse a trend? (2) did the target
accelerate a trend? (3) was the policy instrument itself novel? (4) was the
magnitude of the change required (absolute value, %) above the median for
all 63 targets included? (5) was the annual rate of change required by the
target (absolute value, %) above the median for all 63 targets included? (6)
was the magnitude of the change required (absolute value, %) above the
median for that specific category (federal, RPS, EERS) and (7) was the
annual rate of change required by the target (absolute value, %) above the
median for that category ((federal, RPS, EERS). Each question produced
a binary indicator for the target. In addition, constructing these indicators
produced ratio indicators, e.g. annual % change required, which we used
as well. Second, we created indices of ambition consisting of counts of the
positive values for 5 of the indicators above. We focus our analysis on 4
measures of ambition in Table 2. Robustness checks include assessment of
all 21 ambition variables; The SI provides full definitions and weights used
to generate these.
3.2.4
Timing
Because longevity is a central aspect of the motivation for this study we construct several time related variables. We identify 3 dates: (t1 ) the year the
target was first announced, (t2 ) the year target program was implemented,
and (t3 ) the year in which the ultimate target was to be achieved. We were
unable to access data to accurately characterize t1 in all cases. We defined
11
t2 as Start year and t3 − t2 as Duration.
We also record modifications, extensions, and revisions at later dates for
each target. We assess: were the targets changed? Did they become more
stringent? less ambitious? accelerated or delayed? If they were changed,
what was the rationale? When did the change occur? Any target that
included such a change was coded as Revision = 1.
3.2.5
Geography
One way to account for the heterogeneity in the types of targets included
here is to classify them by their jurisdiction. As indicated in Table 1 we
have four types of targets, which we code as federal, state RPS, state EERS,
and non-U.S.
4
Results
To address question 1, we summarize the data with trends and descriptives.
For question 2, we identify the effects of target characteristics on attainment by regressing the M et variables on a vector of target characteristics.
We include additional specifications in which we identify effects of target
characteristics on ambition.
4.1
Summary descriptives and trends
Table 3 shows summary statistics for the 11 variables used in the regressions
4.1.1
Timing, ambition, and enforcement
For the 63 targets, median Duration was 12 years, with a range of 2–21
years. The SI shows the duration of targets by the year in which they were
12
Variable
Met v1
Met v2
Binding
Discretionary
StartYear
Duration
Revised
Ambition v1
Ambition v2
Ambition v3
Ambition Rate
Table 3: Descriptive statistics
Obs.
Mean
Median Std.dev.
57
0.77
.
0.42
36
0.64
.
0.49
63
0.65
.
0.48
63
0.08
.
0.27
63 2003.41
2007
9.00
63
11.06
12
4.62
63
0.17
.
0.38
63
2.03
2
1.14
63
3.10
3
1.62
63
2.03
2
1.08
63
0.05
0.02
0.09
Min.
0
0
0
0
1974
2
0
0
0
0
0
Max.
1
1
1
1
2013
21
1
4
6
4
0.50
announced. The median of 12 appears to be stable over time.
As discussed above we measure ambition in several ways. Ambition
versions 1-3 provide counts of ambition indicators, defined and weighted
as described in the SI. AmbitionRate—the absolute value of the annual
rate of change required to meet the target—ranges from 0–50%, with a
median of 2%. Ambition appears to be declining over time (Fig. 1). Removal
of apparent outliers in the 1970s would reduce steepness. However, those
targets have been so influential in shaping perceptions of credibility that one
could even justify weighting those higher, which would increase steepness.
The time trends using ambition indexes are also declining and appear more
robust to the removal of the 1970s outliers (see SI). These results suggest a
decline in ambition over time.
Table 4 compares the enforcement of a policy (binding) and attainment
of the target (M et). All targets which were met by their target year had
binding enforcement measures. We see substantial differences between the
columns for binding and non-binding, making these a variable of keen inter-
13
Rate of change required
>50%
Fed.
RPS
EERS
Intl.
0.4
0.3
0.2
0.1
0
1970
1975
1980
1985
1990 1995 2000
Year implemented
2005
2010
2015
Figure 1: Ambition measured as absolute value of annual rate of change
required by each target, by year program began. Line is linear fit to all
targets.
est for the subsequent regressions.
Table 4: Compliance and attainment of target
Non-binding Binding Total
Not met
5
1
6
Met in target year
0
10
10
Met all years
2
11
13
Met latest year
10
11
21
Not met in any year
4
3
7
Not yet
1
5
6
Total
22
41
63
4.1.2
Were targets met?
As an initial descriptive comparison we compare pre-target historical trends,
targets, and post-target outcomes (Figure 2). We group the targets into 5
categories so that each target within a category can be compared using
the same indicator. Where prominent ex ante forecasts are available, we
14
compare them against outcomes. For example, in Figure 2 a, shows 3 targets
for oil imports, set in 1974, 1979, and 2011. This time series shows the failure
to meet the first 2 targets as well as the declining ambition of the targets
over time. The slopes of the dashed target lines represent AmbitionRate.
We show similar figures for targets for fuel efficiency of cars (b), biofuels (c),
SO2 (d), renewable electricity (e,f).
We answer question 1—Have long-term energy policy targets achieved
their goals? —with summaries of the M et variables. Using M etv1, of the 63
targets, 44 attained their goal, 13 did not, and 6 results were dropped. In
M etv2, of the 63 targets, 35 attained their goal, 12 did not, and 27 results
were dropped. The short answer is thus: 64% of the time using the strict
definition of Met and 77% of the time using the definition that credits targets
for being on track to their goals.
4.2
Predictors of target attainment
We regress the met variables on the target characteristics. We use probit
models due to binary dependent variables and use robust standard errors to
calculate p-values. Table 5 shows six specifications, with 1 as our base case.
Models 2 and 3 use alternate definitions of ambition; model 4 adds a dummy
for state RP S; model 5 adds a dummy for revision; and 6 uses the stricter
definition of met. Further, we assess whether these results are robust to
various definitions of ambition by running regressions that substitute in all
21 ambition variables.
A first observation is that the binding variable is significant and positive
in all 6 models This result is robust across all 21 definitions of ambition.
A related observation is that discretionary is always negative, although
15
a. Oil imports
b. Auto fuel efficiency
40
30
10
Actual
Forecast (1973)
Nixon (1974)
Carter (1979)
Forecast (2010)
Obama (2011)
5
0
1970
miles/gallon
million bbl/day
15
1980
1990
2000
2010
20
Historical
CAFE (1974)
CAFE extension
Actual
CAFE (2010)
10
0
1970
2020
1980
1990
c. Biofuels
Historical
EPACT (2005)
EISA (2007)
Actual
million tons/year
million bbl/day
10
1
0.5
8
6
4
2
1990
2000
2010
0
1980
2020
e. California renewables
1990
2000
2010
0.4
Historical
RPS (2002)
RPS (2006)
RPS (2009)
Actual
renewable elec./all elec.
renewable elec./all elec.
Historical, ph. I units
CAAA ph. I
Actual ph. I units
CAAA ph. II
Actual ph. I+II units
f. Germany renewables
0.4
0.2
0.1
0
1990
2020
12
1.5
0.3
2010
d. SO2
2
0
1980
2000
2000
2010
0.3
0.2
0.1
0
1990
2020
Historical
EEG (2000)
EEG (2009)
Actual
2000
2010
2020
Figure 2: Comparisons of historical data, targets and actual performance
after target was set.
16
Figure 3: Coefficient values for Binding and Duration in Base model using
multiple definitions of the Ambition variable. Black lines indicate median
coefficient values. Colors indicate robust p values: black, p<0.01; dark gray,
p<0.05; light gray, p<0.1; white, p≥0.1.
not consistently significant. Second, duration is positive and significant in
almost every model. It is also robust across multiple definitions of ambition.
Third, the ambition variables are always negative but only in some cases
significant. In addition, startyear is positive but insignificant in all but one
case. RP S and revision are insignificant. The SI includes a covariance
matrix and tests of collinearity for these independent variables. There are
no strong correlations and the aggregate variance inflation factor is 1.3, well
below the level of concern.
In order to test the robustness of binding, we compare the coefficients
and p-values of binding and discretionary across all 21 ambition variables.
The robustness results are given in Figure 3. For additional robustness, we
run these regression using a logit rather than a probit and find very small
differences in sizes of effects and none in significance.
17
Table 5: Estimates of effects on target attainment using probit regressions.
Dependent variable is Met v1 in regressions 1-5, and Met v2 in regression 6
a
VARIABLES
Binding
Discretionary
Start Year
Duration
Ambition v1
(1)
Base
(2)
Ambition v2
(3)
Ambition Rate
(4)
RPS
(5)
Revised
(6)
Y=Met v2
1.547***
(0.006)
-1.649*
(0.081)
51.420
(0.169)
1.117**
(0.040)
-0.860*
(0.093)
1.528***
(0.002)
-1.764**
(0.040)
83.472*
(0.064)
1.042**
(0.043)
1.407**
(0.020)
-1.293
(0.153)
36.297
(0.464)
0.990*
(0.079)
1.436**
(0.018)
-1.433
(0.183)
41.020
(0.332)
0.919
(0.204)
-0.969*
(0.065)
1.588***
(0.008)
-1.723*
(0.080)
51.835
(0.156)
1.176**
(0.040)
-0.777
(0.113)
3.002***
(0.001)
Ambition v2
-1.115**
(0.040)
Ambition Rate
-8.327
(0.274)
RPS
0.573
(0.455)
Revised
Constant
Observations
Pseudo R2
Log likelihood
a
-53.683
(0.382)
1.266**
(0.045)
-0.709
(0.279)
-392.6
(0.167)
-635.4*
(0.064)
-277.9
(0.461)
-313.0
(0.331)
-0.600
(0.326)
-395.9
(0.155)
57
0.304
-21.30
57
0.352
-19.83
57
0.303
-21.32
57
0.317
-20.91
57
0.315
-20.97
Robust p values in parentheses. *** p<0.01, ** p<0.05, * p<0.1
18
404.536
(0.387)
35
0.509
-11.05
4.3
Predictors of target ambition
We also identify predictors of ambition—with particular interest in whether
the apparent decline in Fig. 1 is robust to the inclusion of omitted variables.
Table 6 shows the results for 6 models. Models 1–4 use varying definitions
of ambition for the dependent variable. Model 5 adds RP S to model 1
and Model 6 adds RP S to model 4. We regress ambition indices (models
1–3 and 5) on controls using negative binomial regressions since the indices
are counts. We regress the ambition rates in models 4 and 6 using linear
regressions since the rates are continuous.
In general, results here are weaker than in the estimates of met. But the
hypothesis of declining ambition does seem to hold; Start year is negative
and significant in every case except model 2. The discretionary variable is
negative and significant in all regressions, while binding is positive and only
significant when the dependent variable is Ambition v1. This result does not
support the notion that lack of enforcement allows more ambitious policies.
Both timing variables are negative, though start year is significant in nearly
all regressions versus duration which is significant in only regression 4. Once
again, geography was not a significant predictor of ambition and the sign
of its coefficient switches from negative to positive. The other explanatory
variables are not consistently significant.
5
Discussion
In this review of 63 past energy policy targets we find that targets were
met in 64-77% of cases. The lower number uses a strict coding method and
the higher number considers targets met if they are on pace to reach their
target. U.S. federal targets were met 56% of the time, state RPS targets
19
Table 6: Estimates of effects on target ambition: negative binomial regressions for Y=ambition indices (1,2,3,5) and linear regression for Y=ambition
rate (4,6).
a
Binding
Discretionary
Start Year
Duration
(1)
Y=amb.
v1
(2)
Y=amb.
v2
(3)
Y=amb.
v3
(4)
Y=amb.
rate
(5)
Y=amb.
v1
(6)
Y=amb.
rate
0.290***
(0.002)
-1.047***
(0.006)
-31.978***
(0.001)
-0.008
(0.932)
0.150
(0.123)
-0.763**
(0.039)
-10.129
(0.389)
-0.045
(0.535)
0.179*
(0.077)
-0.668*
(0.088)
-19.103*
(0.093)
-0.037
(0.638)
0.004
(0.737)
-0.029***
(0.010)
-9.939**
(0.011)
-0.031**
(0.031)
243.0***
(0.001)
-26.061
77.3
(0.387)
-55.695
145.3*
(0.093)
-51.111
75.7**
(0.011)
0.236**
(0.049)
-0.925**
(0.017)
-33.357***
(0.002)
-0.103
(0.345)
0.172
(0.248)
253.7***
(0.002)
-26.061
0.008
(0.624)
-0.036**
(0.033)
-9.887**
(0.013)
-0.025
(0.145)
-0.010
(0.520)
75.3**
(0.013)
63
63
63
63
0.0327
-65.87
0.0173
-74.05
0.0150
-66.79
63
0.434
.
94.42
63
0.437
.
94.57
RPS
Constant
ln (Alpha)
Observations
R2
Pseudo R2
Log likelihood
a
Robust p values in parentheses. *** p<0.01, ** p<0.05, * p<0.1
20
0.0347
-65.73
95%, and state EERS targets 71%. We did not have comprehensive coverage
on non-U.S. targets so make no claims about reliability of those frequencies.
5.1
Interpretation
The most robust significant predictor of success in meeting targets was
whether the targets had enforcement mechanisms, such as penalties or fines
for non-compliance (binding). In some cases, these enforcement mechanisms
included clauses that gave regulators discretion over whether to impose a
penalty due to non-compliance. We find only 5 cases where enforcement was
discretionary so it is unsurprising that this variable was less significant than
binding. But the size of the coefficients for discretionary are very close to
those of binding in the 6 models shown in Table 5. These two effects merit
further investigation, as adding the coefficients together indicates that discretionary clauses completely offset the effect of enforceable penalties on
target attainment.
We find support for the notion that longevity is needed in policy making because long-lived capital stock makes energy systems slow to change.
The coefficients on duration indicate that the longer the time horizon over
which a target is to be achieved the higher the likelihood of achieving the
target. This result does not support the more cynical interpretation that
policymakers prefer long term targets because they expect that they will
not be held accountable for meeting them. Some of these policies are quite
long; our median time to meet a target was 12 years. This result even holds
in model 6, which employs the strict coding of met, in which most of the
longer term RPS and EERS targets are excluded because they have been
more recently implemented.
21
In line with intuition, targets with lower ambition were more likely to
be met. We were particularly careful about assessing the robustness of the
results to several formulations of ambition—both because there are multiple
dimensions to ambition and because the targets are heterogenous. Although
ambition had a negative effect in every model in Table 5, it was not always
significant. As shown in the SI, we also ran regression using all 21 definitions
of ambition. Ambition was negative in 17 cases and significant in 13 of
these. Note that this range in the effect of ambition does not affect the
interpretation of the variables for binding and duration, which both remain
stable across the 21 definitions of ambition (Fig. 3).
Our assessment of the predictors of target ambition produced results with
generally lower reliability than those for target attainment (Table 6). Our
most robust result from these models is the negative coefficient on start year,
indicating that ambition has been declining over time. Thus the declining
ambition observed in Fig. 1 is not entirely attributable to the outlier oil
import targets from 1974 and 1979. There has been a real reduction in
ambition over these 40 years that is robust to what aspects of ambition are
included: rate of change required, absolute change required, policy novelty,
as well as comparing the target to pre-existing trends.
5.2
Open questions
These data and results are intended to provide the beginnings of an empirical
basis for understanding credibility in energy policy making. They raise
additional research questions, which are likely to require new data, new
types of data, and new methods, including qualitative ones.
A primary question is whether this history affects the present: To what
22
extent does the history of attainment of past targets affect credibility and
incentives today? A hypothesis here is that memories in the energy industry
are long. But it is possible that beliefs are more influenced by peers and by
more immediate conditions than by the past. Addressing this question might
require interviews to understand how investors form their beliefs about the
credibility of future targets.
Do aspirational targets help or hinder outcomes? Are goals with high
ambition, but that have a high likelihood of failure, helpful in orienting behavior in a socially-beneficial direction—or are such goals distracting and
damaging to future credibility and incentives? Examples include “technology roadmaps” in Japan (electric vehicles), U.S. Dept. of Energy (Solar),
and the International Energy Agency (carbon sequestration). Others include stringent climate change targets such as 2 degree stabilization (Randalls, 2010). Evaluating such goals likely requires assessment of the long
term consequences of setting goals, especially those that were not achieved
but which produced benefits. For example, targets may also send signals
that catalyze actors (Tews and Kerstin, 2004) and may generate knowledge,
such as the U.S. Synfuels Corporation, which never approached its production targets but generated gasifier technologies in widespread use today
(Anadon and Nemet, forthcoming).
How strictly should we adhere to commitments? This involves the tradeoff between commitment and flexibility discussed above. A key challenges
here is distinguishing between competing social priorities that create a legitimate basis for changing a commitment, and changes that eflect the ascension
of narrower private interests over broader and longer term public ones. It
is a central open question for ongoing work in this area. One avenue for
negotiating this trade-off has been to include discretion, but insulate it from
23
expedience and political pressures by establishing an independent institution. This is comparable to the way a central bank makes decisions about
the money supply. Suggestions of independent entities in the energy realm
include a Strategic Petroleum Reserve Corporation for deciding about the
levels and withdrawals from the strategic petroleum reserve (Blumstein and
Komor, 1996); an energy and climate agency to pursue climate targets while
maintaining energy security (Helm et al., 2003); and a Carbon Market Efficiency board to adjust CO2 permit allocations while pursuing long term
targets (EPA, 2008).
5.3
Conclusion
Those who are skeptical of the credibility of long term commitments on
energy policy can find justification for their perceptions in the results of this
study. U.S. energy targets have been missed a quarter to a third of the time.
One reason that this skepticism may be excessive is that perhaps we
have learned from the experience of the past 40 years and have a more substantial basis for making future commitments today. Signs of such a shift
appear in these data, albeit only weakly; there is a consistent positive sign
on start year in Table 5. It is in most cases insignificant but does reveal
some improvement in goal attainment. There is though also a trend toward
less ambitious targets in Table 6, which is quite robust. Rather than a sign
of discouragement, reduced ambition may comprise a promising strategy
for improving the credibility of future commitments. Credibility is fragile and the best avenue may be to rebuild it gradually, by establishing a
series of intermediate term targets of modest but steadily increasing ambition, while assimilating new information (Haasnoot et al., 2013). However,
24
a reduced-ambition strategy is at best an interim solution for addressing
energy problems for which ambition is necessarily large. Ultimately ambition needs to be commensurate with the scale of problems we face, including
energy poverty, energy security, air pollution, and climate change. The success of a reduced-ambition strategy ultimately depends on the feasibility of
increasing ambition later, once credibility has been re-established, but not
so much later that problems become intractable.
These results suggest that a second strategy for enhancing credibility
is a blunter one—simply to increase ambition and aggressively apply enforcement mechanisms. In the results here, clauses that included financial
penalties or other means of enforcement were the dominant factor in determining whether a target was met or not. However, typically, reduced
discretion comes along with increased enforcement, as does a consequent
reduction in the ability to respond to conditions that change. Such a strategy may be better suited to a future world characterized by stability and
moderate uncertainty than to a dynamic deeply uncertain one.
The tradeoff between the benefits of flexibility and the benefits of commitment remains and other avenues to strengthening the incentives for long
term investment surely exist. They would be informed by subsequent research that explores the way that perceptions of credibility are actually
formed and how actors apply their perceptions to decisions. For any strategy involving some enhancing of credibility surely it would help to more
transparently and accessibly track progress toward our societal goals. Building public confidence and developing the semblance of broad consensus will
require transparent evidence of progress toward achievable measurable results. Energy targets have and will involve prominent societal goals and
should not rely on academics and graduate students to evaluate the most
25
basic dimensions of their outcomes.
Acknowledgments
This paper has benefitted from comments received at the Association for
Public Policy Analysis and Management Fall Conference, the University of
Wisconsin, and Georgetown University.
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