Abstract:
We study optimal taxation in a dynamic stochastic general equilibrium model in which agents are concerned about model uncertainty. We assume that an externality through global temperature changes resulting from greenhouse gas emissions (GHG) adversely affects the economy's capital stock and, thus, its output. The precise effects of this externality, however, are subject to uncertainty. Most existing approaches, however, only incorporate the uncertainty associated with climate change in a limited way (Stern, 2013). To fill the gap, we focus on the implications of this uncertainty. In order to model the effect of the emissions created by economic activity on the environment, we employ the framework used in Golosov, Hassler, Krusell, and Tsyvinski (2013, GHKT hereafter).1 While they assume that the mapping from climate change to damages is subject to risk, in our model this mapping is subject to Knightian uncertainty. We study the implications of this assumption using a robust control approach. We believe that this is an appropriate application of uncertainty in economic modeling. After all, man-made climate change is unprecedented, and there is an ongoing heated debate about its potential effects. Although our model does not include the risks of large-scale human migration or conflict resulting from climate change, it proposes a robust control approach as an alternative to standard probability distribution-based modeling. More specifically, concerned about model uncertainty, a social planner in our model maximizes social welfare under a "worst-case scenario."
In addition to taking model uncertainty into consideration, there are two additional differences between our assumptions and those in GHKT. First, we find it convenient to assume that the environmental externality indirectly affects output through the capital stock. As a result, the theoretical analysis in our model brings different results, although the two assumptions lead to identical results if we assume 100 percent capital depreciation (as we do in the computational part). A second difference is that our estimates of total fossil fuel supplies are significantly larger than theirs. This is partly due to the addition of the supply of unconventional oil and gas, but mainly due to our consideration of estimated methane hydrate resources.2
Under additional assumptions, we obtain a sharp analytical solution for the implied pollution externality, and we characterize dynamic optimal taxation. A small increase in the concern about model uncertainty can cause a significant drop in optimal energy extraction. The optimal tax, which restores the social optimal allocation, is Pigouvian. Under more general assumptions, we develop a simple recursive method that allows us to solve the model computationally. We find that the introduction of uncertainty matters in the sense that our model produces results that are qualitatively different - for example, in terms of oil consumption - from those of GHKT. At the same time, concerns about uncertainty do not affect renewable energy adoption. The reason is that, rather than being driven by renewable energy use, the margin that determines short-term decisions regarding energy sources is driven by two factors: the trade-off between higher versus lower total energy consumption, and the choice of coal versus gas/oil. We find that oil use in our model can be flat for some parametrizations. We study optimal output growth in the presence and in the absence of concerns about uncertainty and find that the results can be very different. In the worst-case scenario, optimality implies that a small sacrifice in yearly output can prevent a large future welfare loss.
As the green energy sector does not create emissions in our model, we find that the optimal path for the use of green energy does not directly depend on the level of concern about model uncertainty. However, since green energy, coal, and oil are substitutes, model uncertainty does indirectly affect the use of green energy, through its impact on coal and oil. We also find that an increase in the concern about model uncertainty causes a significant decline in the use of coal, while the use of oil is slightly delayed. Holding other parameters fixed, the optimal path of oil consumption is jointly determined by the resource scarcity effect and the model uncertainty effect. Naturally, we do not find a significant difference in oil consumption when the scarcity effect dominates. However, when we consider a higher level of initial resources of fossil fuel, the concern about model uncertainty substantially discourages the use of oil.
Kolstad (1996) discusses uncertainty in integrated assessment models but does not employ techniques from robust control. Existing work that employs robust control or related techniques in order to address issues related to model uncertainty includes Hennlock (2008, 2009), Funke and Paetz (2010), Sterner and Hennlock (2011), and Lemoine and Traeger (2011). These papers employ a version of Nordhaus's DICE model and we build our analysis closely on GHKT (2013), which is consistent with the DICE model. Using GHKT allows to derive analytical results under a set of additional assumptions. In related recent work, Weitzman (2014) considers the social costs of carbon when catastrophic climate-related events follow a fat-tailed distribution.3 In addition to building on GHKT, our paper relies on existing work in robust control theory from both economics and engineering. In the traditional stochastic control literature, uncertainties in the system are modeled using probability distributions. The goal there is to derive a policy that works best "on average." In contrast, given a bound on uncertainty, robust control is concerned with optimizing performance under a so-called worst-case scenario.4 Hansen and Sargent (2001) introduce techniques from robust control theory to dynamic economic decision making problems.5 They point out the connection between the max-min expected utility theory of Gilboa and Schmeidler (1989) and the applications of robust control theory proposed by Anderson et al. (2000) and Dupuis et al. (1998). Hansen, Sargent, Turmuhambetova and Williams (2006) give a thorough introduction to the robust control approach. They discuss applications to a wide range of problems within the linear-quadratic-Gaussian framework.6
As is standard in the robust control literature, our paper postulates the problem of optimal fossil fuel extraction as a two-person zero-sum dynamic game: in each stage, a social planner (a representative household in the decentralized version) maximizes social welfare (lifetime utility) by choosing the level of energy extraction, consumption, labor and capital investment. Subsequently, a malevolent player chooses alternative distributions in order to minimize the respective payoff. Our work contributes to the existing literature of applications of robust control in economics in two ways. First, it explores a class of models under a non-quadratic objective and non-linear constraints. In that regard, we demonstrate that models of the type used in GHKT (2013) can be restated in a robust control framework. We then derive some sharp analytical results, and compute the resulting model numerically. Second, we employ the exponential distribution as the approximating distribution. While existing studies usually employ the linear-quadratic model combined with Gaussian distributions in order to produce analytical solutions, our work shows that the approximating distribution for models with log-utility and full depreciation of capital can be drawn from either the normal or the exponential family.
The paper proceeds as follows. Section 2 presents the basic model. Section 3 studies the model analytically, while Section 4 presents our numerical and quantitative findings. A brief conclusion follows. Technical material appears in the appendices.
In order to characterize the optimal policy for the case where there is a concern about climate change and model uncertainty, we first formulate a general framework for the robust planner's problem, a benchmark that we will subsequently compare to decentralized market solutions.
Time, , is discrete and the horizon is infinite. The world economy is populated by a
-continuum of infinite-lived representative agents with utility
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This leads to the following social planner's problem:
In this section, we will make the following additional assumptions. While these assumptions are admittedly strong, they allow us to fully solve the model analytically. As we shall see, certain aspects of the solution remain instructive in the next section, when the restrictive assumptions are dropped and the model is solved numerically.
(A1) The period utility function is given by
.
(A2) Capital depreciates fully; i.e., .
(A3) The production function is given by
.
(A4) The damage function is given by
.8
(A5) The approximating distribution for is exponential with mean
and variance
; i.e.,
.9
(A6.1)
.10
(A6.2) .
(A7) There is a single fossil energy sector producing oil at zero cost. Production is subject to a resource feasibility constraint:
. As a result,
and
.
(A8) There is no population growth, and the aggregate labor supply is normalized to 1 . That is, and
in all periods.
(A9) There is no technology improvement. That is, is constant over time. We normalize
.
(A10) The resource feasibility constraint is not binding.
We will first solve the social planner's problem. We will then discuss the decentralized problem and show that the socially optimal allocation can be restored by implementing fossil fuel taxes on the energy-producing sector.
Under A1-A10, the social planner's problem can be rewritten as:
First, we define the robustness problem (the inner minimization problem) by
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A few technical remarks are in order. First, the function is increasing in
, decreasing in
, and jointly concave in
and
. The value of
is the same as in the model without concern about model uncertainty.
Both
and
are affine functions of
. In addition, it can be shown that,
given
, both
and
are increasing functions of
. This is intuitive since a
greater
implies a larger resulting penalty from a deviation of
from
its approximating distribution, and thus a lower concern about model-uncertainty. Note that
is affected by
only through
. This is due to logarithmic utility. As a result, a greater concern about model-uncertainty will lower both
and
. The value of the externality from one unit of emissions evaluated at
is given by
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Another natural measurement for model uncertainty is the distance between
and
,
, given by the relative entropy
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Robust control modeling can be introduced in a variety of ways. So far, we have used a closed-loop zero-sum dynamic game in which the social planner moves first in each period. Alternatively, we can construct a game with the same information structure by interchanging the order of and
in equation (8). The two games differ only in terms of the
timing protocol. However, both lead to the same (unique) feedback saddle-point equilibrium if certain conditions are satisfied. More precisely, if (A1)-(A10) hold, then the objective in (8) is strictly concave in
and
, and strictly convex in
. Consequently, the two closed-loop zero-sum dynamic games admit the same unique pure strategy saddle-point Nash equilibrium, which is the one described in Proposition 1.
Let us now turn to the decentralized problem. Suppose a percentage tax, , is imposed on emissions,
. Since the extraction cost of energy (the cost of creating emissions) is zero, it must be true that
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In this section we first extend the analytical model by relaxing assumptions (A6.1) and (A6.2). For our baseline model, we will assume that
, the approximating distribution of
, is exponential. As we now
allow for
, we need to introduce two additional state variables (
and
), since keeping track of the sum
will no longer suffice. We will also
relax (A7) by incorporating a coal and a green sector into the model. Furthermore, we will relax (A8) and (A9) by allowing
and
to grow at a rate of 2 percent per year. Last, we will drop (A10).
The social planner's problem becomes:
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Finally, the first-order condition for yields
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Parameter | ![]() |
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Value | 0.0228 | 0.2 | 0.393 | 0.3 | 0.04 |
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Parameter | ![]() |
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Value | 103 | 699 | 800 | 0.5008 | 0.08916 | ![]() |
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Figure 4 describes the optimal paths for the use of green energy, coal, and oil, as well as the resulting carbon concentration in the atmosphere, conditional on different levels of concern about model uncertainty. For simplicity, we refer to the optimal path under
as the non-robust optimal path, and to the path under
as the robust optimal path. Since the green energy sector does not inject carbon into the atmosphere, the optimal path for the use of green energy does not directly depend on the
level of concern about model uncertainty regarding the externality from carbon emissions. However, since green energy, coal, and oil are substitutes, through its impact on the "dirty" energy sectors, model uncertainty considerations do affect the use of green energy indirectly.
We find that an increase in the concern about model uncertainty causes a significant decline in the use of coal. In contrast, the use of oil is delayed, but only slightly. As the supply of oil is finite, the decline rate of oil-use depends not only on model uncertainty, but also on resource
scarcity. As we will show in the next section, an initial stock of oil equaling
is low enough so that the resource scarcity effect overwhelms the model uncertainty effect in determining the optimal use of oil in the economy. This explains why we do not
observe a sharp decrease in the optimal use of oil when the concern about model uncertainty increases. Finally, straightforward calculation shows that the difference in energy use in the two optimal paths leads to a significant difference in the associated carbon accumulation. Our model predicts
that if there is a small concern about model uncertainty (
), or if model uncertainty is not incorporated into the model (
), atmospheric carbon concentrations will reach a level as high as
(net of preindustrial levels) after 180 years. However, this number is reduced by 40 percent to about
if concerns about model uncertainty are incorporated and addressed through the corresponding optimal tax, restoring the optimal energy path under
.
Figure 5 demonstrates a direct consequence of the above analysis: based on the mapping from carbon concentrations to global temperatures used in the RICE model,
, the global average temperature will rise by 3.8 degrees Celsius 180 years from now if the concern about model uncertainty is addressed, and by 5.3 degrees Celsius otherwise.
The graphs in the first (second) column in Figure 6 describe the paths of total damages as a percentage of the capital stock, as a function of the capital stock and output, assuming that the approximating model (worst-case model) for is the true model.11 In each graph, the green-dashed line (blue-solid line) represents the outcome
when energy is extracted based on the non-robust (robust) optimal path. The main findings can be summarized as follows. If the approximating model for
is the true model, pursuing the
robust optimal path for energy consumption would further reduce total damages by an additional 1 percent 180 years from now. However, due to a more conservative use of oil and coal in the
final good sector, such a policy will also reduce both capital stock and output in the long run. Since utility depends only on consumption (which is proportional to output), this implies that the welfare loss from over estimating the concern about uncertainty would be rather small. In contrast, if
the true distribution of
evolves according to the worst-case model in each period (second column of Figure 6), the cost of implementing the non-robust optimal policy is rather large.
In fact, the non-robust policy, which overlooks concerns about model uncertainty, will dramatically reduce the entire capital stock in 120 years, resulting in a large reduction in output and welfare.12
Here we further explore the implications of assumption (A5). To this end, we now assume that the approximating distribution of is normal with mean
and variance
; i.e.,
. This creates two key differences. First, the normal distribution provides us with two degrees
of freedom: the mean,
, reflecting the planner's prior expectation regarding damages, and the variance,
, indicating the prior regarding model uncertainty. In comparison, recall that the exponential distribution only used one parameter,
, which determined both the mean and the variance of
. As we shall see below, assuming that
is normally distributed can also eliminate the "breaking point" for
, which is
always present when
follows an exponential. This is because the exponential distribution has a "fat"tail, thus allowing more room for nature to create a worst-case-scenario given a
level of penalty,
. We have
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Below we plot the same quantities as those shown in Figure 4 through Figure 6, but under the assumption that the approximating distribution of is normal. Our focus here is to
compare the effects of model uncertainty on optimal oil use under different values of
. As we have discussed earlier, holding other parameters fixed, the optimal path of oil
consumption is determined jointly by the resource scarcity effect and the model uncertainty effect. First, note that we can hardly identify a difference between the robust and the non-robust optimal paths for oil-consumption when the scarcity effect dominates, that is, when
is sufficiently small. Figure 7 shows that when
, the non-robust optimal paths replicate their counterparts in GHKT. In this case, model uncertainty delays the optimal use of oil only slightly. However, Figure 10 displays
an altogether different pattern. When
is set to
, although both
paths are still decreasing over time, model uncertainty discourages the use of oil substantively. Finally, as
goes to infinity, as shown in Figure 12, we observe a qualitative
difference between the two paths. On the one hand, the non-robust optimal path allows the use of oil to grow unboundedly, partially due to the technological progress in the coal and green sectors. On the other hand, the increasing trend in oil consumption is curbed due to the externality caused by
carbon emissions.
We now turn to a comparative analysis of the damages resulting from fossil fuel consumption. GHKT assume
and estimate damages of $56.9/ton of carbon using an annual discount rate of 1.5% and $496/ton under a rate of 0.1%. When
, and if there is no concern about model uncertainty (
), the welfare loss implied by our model equals
ton. This number is independent of the approximating distribution for
, the initial stock of oil, and the future path of the GHG concentration. When
, however, these factors can matter substantially, as seen below. If the approximating distribution is normal, the losses are given in the following table.
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0.01 | 0.1 | 1 | 100 | ![]() |
253.8 GtC | 239.60 | 70.65 | 50.85 | 48.52 | 48.49 |
8000 GtC | 276.60 | 90.60 | 55.08 | 48.57 | 48.49 |
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318.70 | 103.06 | 63.42 | 56.49 | 48.49 |
In order to further explore the model's implications, we now report the results for the limit case where oil is in infinite supply, while coal is constrained under an initial stock
. This case demonstrates that the optimal use of oil mimics that of the case in which both oil and coal are in infinite supply. In addition, the use of coal increases
steadily at the beginning and then starts to drop.
We studied optimal taxation in a dynamic stochastic general equilibrium model where agents are concerned about model uncertainty regarding climate change. We used robust control theory in order to model the uncertainty associated with climate change. Our work builds heavily on the model introduced in GHKT. While admittedly restrictive, this framework allows us to derive an analytical solution. In contrast to the existing literature, we used an estimate of fossil fuel that includes methane hydrates as part of the supply of unconventional natural gas. While this huge resource is not readily available with today's technology, we believe that it is appropriate to include it given the long-term modeling that we follow throughout this exercise. Finally, we assumed a fat-tailed distribution of damages as a way to capture the extreme effects discussed in Stern (2013).
We obtained a sharp analytical solution for the implied externality, and we characterized the optimal tax. We found that a small increase in the concern about model uncertainty can cause a significant drop in optimal energy extraction. The optimal tax which restores the socially optimal allocation was shown to be Pigouvian. Under more general assumptions, we developed a recursive method that allowed us to solve the model computationally. We showed that the introduction of uncertainty matters in a number of ways, both qualitatively and quantitatively. This dependence relies heavily on specific assumptions about the magnitude of fossil fuel reserves. As our model is based on GHKT, it is worth discussing some of the main differences in our results.
Several of the variables in the model developed in GHKT can be thought of as being subject to uncertainty. These include the variables governing the dynamics of CO concentration,
those governing productivity growth and hence future production, the costs of alternative sources of energy (coal, oil-&-gas, and renewable), and, finally, the damages caused by the concentration of atmospheric CO
. In this paper we concentrate on the uncertainty associated with damages from CO
concentration. As in GHKT, we conclude that the consumption of coal
should be constrained. However, as we considere a higher stock of hydrocarbons, we derive different results regarding total consumption of fossil fuel. As a result, we showe that under a less binding resource constraint, hydrocarbon use declines significantly as the concern about model uncertainty
increases.
The core theoretical result in GHKT is that, when expressed as a proportion of GDP, the optimal tax on CO emissions depends only on the discount factor, the measure of the expected
damage, and the depreciation of atmospheric CO
. In particular, the tax rate is independent of the stochastic value of future output and the stock atmospheric CO
. They derive this result based on three main assumptions: (i) logarithmic utility (which implies constant saving rate), (ii) the climate damage is proportional to GDP and has constant elasticity
with respect to the level of atmospheric CO
, and (iii) the stock of CO
is linear in past and current emissions. We show that once we consider model uncertainty, the Pigouvian tax can implement the optimal allocation as in GHKT. However, the expected level of damage is no longer sufficient for determining the optimal tax. Specifically, the
optimal tax rises as the concern about uncertainty increases, even though the expected damages remain unchanged.
Our model can be extended in many ways. For comparison purposes, we tried to stay close to the parametrization used in GHKT. We could study versions of the model under different parametrizations. In the current version, the growth rate of renewables is assumed to be independent from the concern about model uncertainty. It would be interesting to endogenize growth in renewable energy productivity. A related extension could involve using a distortionary tax on labor to subsidize R&D in renewables in order to study the effects on energy composition and growth. Additionally, we could study a benchmark case where coal supply is constrained, while assuming infinite supply of gas and oil. Finally, at the cost of significant additional computational complexity, we could consider more involved climate dynamics.
We demonstrate that the optimal level of GHG, , has the following properties:
and
, where
is the upper bound for entropy allowed in the constraint game.
Proof: Recall that
and
, where
. Define
and
. It follows immediately that
is decreasing in
. In addition, since both
and
are functions of
, it follows that
is a function of
:
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Here we discuss the equivalence between the recursive Stackelberg game and its date-0 counterpart. We concentrate on the one-sector model. In the recursive version of the Stackelberg game, the worst-case model for
depends on the endogenous state
and on the choice variable
. This feature can be difficult to interpret.14 Alternatively, we can construct a date-0 Stackelberg game in which the malevolent player, as the leader of the game, chooses the distorted models of
,
, first. This leads to
being independent of the endogenous states. We then show that, on the equilibrium path, the worst-case models derived from the date-0 Stackelberg game coincide
with those derived from the recursive game. We demonstrate the equivalence by using the big
little
result as is in Chapter 7 of Hansen and Sargent (2008).
Consider the date-0 Stackelberg game in which, at date zero, the minimizing player chooses the distorted probability process
, followed by the maximizing player choosing the control process
:
We introduce an exogenous state vector process
which evolves as:
Define the distorted process
as
Given the above exogenous distorted process, the maximizing player chooses at date zero to maximize the social welfare given in equation (18). With the aid of
the exogenous state, this maximization problem can be expressed in a recursive form as:
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We proceed to find the solution to this date-0 problem of the maximizing agent given the distorted process
in equation (22). Then we will argue that this solution is identical to the Markov perfect equilibrium of the sequential game defined in the
main text. We implement a guess-and-verify method. We first guess that
takes the form
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Furthermore, we guess that takes the form
where
and
are undetermined coefficients. After some tedious derivations, we obtain
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Here we provide a brief description of our numerical procedure. Assume (i) 100 percent capital depreciation, (ii) Cobb-Douglas production function, and (iii) exponential damage function. Then, it follows from the analysis in Sections 3 and 4 that the value function given in equation (61) takes the form
We then solve for
using a 4-dimensional Chebyshev polynomial approximation method. The above simplification has significantly reduced the computational burden of solving a dynamic max-min game,
allowing us to utilize the parallel toolbox of MATLAB on a 8-processor computer. Table 1 and Table 2 below report the grid specifications used in the complete model, as well as its variations for
and
, respectively.
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6 | 6 | 6 | 6 | 6 |
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7 | 7 | 7 | 7 | 7 |
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10 | 10 | 10 | ![]() |
10 |
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10 | 10 | 10 | 10 | 10 |
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6 | 6 | 4 | 6 | 6 |
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7 | 7 | 4 | 7 | 7 |
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10 | 10 | 30 | ![]() |
10 |
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10 | 10 | 10 | 10 | 10 |
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