Abstract
This article quantifies the impact on optimal climate policy, of both damage elasticity and equilibrium climate sensitivity uncertainty, under separable preferences for risk and intergenerational inequality. The primary findings are as follows. (1) Such preferences can depress the social cost of carbon (SCC) when calibration aims at matching actual economic outcomes, countering the prevailing view that the SCC is greater with separable than with conventional entangled preferences. (2) Damage elasticity uncertainty has larger effects on climate policy than equilibrium climate sensitivity uncertainty, even under high impact tail risk of the latter. (3) Risk aversion decisively strengthens optimal climate policy under joint damage and climate sensitivity uncertainty, than with a single source of uncertainty alone. Indeed, failing to account for the interaction between damage and climate sensitivity uncertainty underestimates the cost of climate change by more than US dollars 1 trillion.
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Notes
The baseline DICE 2013R model for instance predicts a SCC that would start about 2005 US $21 in 2020, and rise monotonically to about 2005 US $143 in 2100. In the latest version of DICE, DICE 2016R, the SCC starts at about $31 and rises at \(\sim 3\%\) for much of this century. The increase in SCC is primarily explained by an updated carbon cycle with a higher climate sensitivity that better tracks the deaccumulation of CO2 over a 4000-year timescale.
Contrary to a prescriptive calibration where model parameters may be chosen on normative grounds, a descriptive calibration emphasizes choosing model parameters such that the model solution is fully consistent with actual economic outcomes.
To achieve this, I use the pure rate of time preference as the degree of freedom when targeting the observed savings rate.
That is, when solving for the optimal policy, I account for the right-tail realizations of equilibrium climate sensitivity.
Dietz and Stern (2015) and Hwang et al. (2013) also consider climate sensitivity uncertainty for various specifications of the damage function under traditional expected utility preferences. Their results suggest that abatement and the social cost of carbon can be more responsive to climate sensitivity uncertainty for very steep damage functions.
They model abatement inertia by putting a constraint on the growth rate of investment and catastrophic climate risk through a collapse in the Atlantic Thermohaline Circulation.
Key here is that current abatement can indeed mitigate future consequences. If the planner were to end up in a highly undesirable state of the world, and abatement has little effect on mitigating future outcomes, it is possible that little to no abatement becomes the dominant strategy. These views find support in the results of Hwang et al. (2016).
While the literature often uses Monte Carlo methods to approximate the impacts of decision making under uncertainty, Crost and Traeger (2013) argue and present evidence for why this can be misleading.
An exception in this regard is Rudik (2019), who uses data gathered by Howard and Sterner (2017) to estimate the parameters of the damage function. He recovers an estimate for \(b_{1}\) that is twice in size that of DICE and an elasticity for \(b_{2}=1.8\), which is very close to the value used in DICE.
Annan and Hargreaves (2011) show that under reasonable assumptions, much greater confidence in a moderate value for climate sensitivity is easily justified, with an upper 95% probability limit for it easily shown to lie close to \(4\,^{\circ }\text {C}\), and certainly well below \(6\,^{\circ }\text {C}\).
Over the years, DICE’s estimate for equilibrium climate sensitivity has in fact been adjusted upwards with the latest 2016 version having a value of 3.1 (Nordhaus 2017).
Stern (2007) uses a pure rate of time preference of 0.1% for his analysis. This compares with 1.5% that DICE generates under a descriptive calibration.
The expression makes use of the assumption that consumption shocks are idiosyncratic. See the Online Appendix for derivation.
There is of course some tension when calibrating the social planner framework to actual economic outcomes. Indeed, the global economy is decentralized with several distortions, imperfections, externalities, and inefficiencies in markets. Such frictions drive a wedge between the social discount rate (i.e., derived from what is socially optimal) and the investment discount rate (i.e., actual returns on risk-free capital assets), meaning that the two are generally not equalized. Should one then still calibrate the social planner model as though it were fully consistent with observed economic outcomes? Arrow (1999), Arrow et al. (2013), Dasgupta (2007), Drupp et al. (2018) and Nordhaus (2007) discuss this matter, and decide in favour of the descriptive approach.
Since \(\rho\) is unobserved, and its value is typically assumed in empirical work, it provides the natural degree of freedom for calibrating the model when the savings rate is fixed.
DICE presumes the availability of negative emission technologies sometime in the future, a feature that is terminated in this paper’s version of the model.
A key requirement in stochastic programming is nonanticipativity (Shapiro et al. 2009). This in essence restricts choices to depend on the current state and not on the future’s yet unrealized outcomes. This requirement ensures that scenarios sharing a common history, generate similar outcomes. In stochastic DICE, this implies a single optimal outcome for consumption, abatement, emissions, and hence a unique path for all state variables prior to the realization of the shock. The reported expected SCC is obtained as the probabilistic sum over the scenario SCCs.
For \(\gamma <\vartheta\), the planner is willing to sacrifice welfare for the early resolution of uncertainty even if this resolution is not pay-off relevant. With \(\gamma =\vartheta\), the planner is indifferent about when risk is resolved (i.e., temporally risk neutral), but prefers the late resolution of risk (i.e., temporally risk-loving) when \(\gamma >\vartheta\). For further details see e.g., Epstein and Zin (1989, 1991), Kreps and Porteus (1978), Traeger (2009) and Weil (1990, 1989).
The savings rate in the deterministic version of the original DICE model by Nordhaus and Sztorc (2013), is also 26.9%.
Lowering \(\gamma\) to 0.8 explains nearly 150% of the initial increase in the SCC and increasing \(\vartheta\) to 10 from 0.8 explains the remaining nearly 40%.
With \(\gamma =0.8\) and recalibration of the base year savings rate disregarded, the model predicts a base year social consumption discount rate of 2.6%.
For brevity, the ensuing discussion focuses on the descriptive calibration, but impacts on the SCC for the prescriptive calibration are illustrated in Fig. 5.
Because \(\text {SCC}_{t}=\nicefrac {\partial F_{t}}{\partial E_{t}}\), greater emissions correspond to a smaller gain from marginal emissions and thus a smaller expected SCC.
In particular, while DICE sets the parameter that calibrates atmosphere inertia to 51 years, in this counterfactual simulation the parameter is set to 21 years.
The present model cannot be directly compared to the versions used by Lemoine and Rudik (2017b), Jensen and Traeger (2016), and Hwang et al. (2017). Nonetheless, when uncertainty is introduced linearly by assuming the ratio (\(\nicefrac {1}{\zeta }\)), rather than non-linearly by taking only the component \(\zeta\), as uncertain, the SCC is always lower under uncertainty with the former. In particular, it lower by between US$ 2-3 through out the years.
In their setting of fat tails, the mean is technically undefined and thus their use of the mode.
Indeed, I observe a greater SCC under uncertainty, when a ‘modal’ value of 3.0 (rather than the mean of 3.56) is used to recover the deterministic policy. For the baseline and benchmark scenarios, the SCC under uncertainty is greater, relative to the deterministic SCC, by US$ 4.657 and 3.889 in 2020, rising to US$ 18.226 and 15.322 in 2075. In specification ‘\(\gamma =\vartheta =0.8\)’ ( ‘\(\gamma =0.8\)\(\vartheta =0.2\)) the SCC is greater by US$ 3.354 (3.322) in 2020, rising to 10.343 (10.051) in 2075.
They model inertia by constraining attainable cumulative abatement per unit change in time.
Figure 5 shows that this pattern continues to hold even when the recalibration of the discount rate is disregarded for the non-baseline specifications.
In fact, with an exogenous Gaussian process for consumption growth and fixed climate policy, it can be shown that the conditional variance of future welfare outcomes reduces present-day welfare for \(\vartheta >\gamma =1\).
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Acknowledgements
The article benefited immensely from comments by two reviewers and the editor (David Popp). Additional comments received at the World Congress of Environmental and Resource Economists are appreciated.
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Appendices
Appendix 1: Extra Graphs
Appendix 2: Simplified DICE Model
DICE’s atmospheric temperature Eq. (2), can be reorganized as:
where \(\Delta _{T}=z_{1}\nicefrac {\xi }{\zeta }-z_{1}z_{2}\). It is immediate that for \(\iota >t\), \(T_{\iota }\) increases in climate sensitivity, \(\zeta\), at a decreasing rate. DICE sets \(z_{2}=0.008624\), which for simplifying the derivation of the SCC can be set zero, yielding:
The parameter \(z_{1}\) defines the inertia in the climate system. DICE2013 sets this value to 0.098, corresponding to a duration of 51 years. In the counterfactual experiment testing the impact of climate inertia, this parameter is set to 0.25.
Furthermore, it is convenient to disregard the upper and deep ocean carbon sinks and only focus on the atmospheric stock (Jensen and Traeger 2016). The simplified stochastic DICE model becomes :
s.t.
Let \(V_{t}\equiv V\left( K_{t},M_{t},T_{t}\right)\)
Taking FOC with respect to \(C_{t}\) yields:
and with respect to \(E_{t}\):
which is an expression for the SCC.
Finding the quantities \(\nicefrac {\partial V_{t+1}}{\partial M_{t+1}}\) and \(\nicefrac {\partial V_{t+1}}{\partial T_{t+1}}\) requires differentiating the value function.
First consider \(\nicefrac {\partial V_{t+1}}{\partial T_{t+1}}\):
For \(\nicefrac {\partial V_{t+1}}{\partial M_{t+1}}\)
Recall that
Letting \(\frac{\partial M_{t+1}}{\partial E_{t}}=1\) gives the expression in the text.
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Okullo, S.J. Determining the Social Cost of Carbon: Under Damage and Climate Sensitivity Uncertainty. Environ Resource Econ 75, 79–103 (2020). https://doi.org/10.1007/s10640-019-00389-w
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DOI: https://doi.org/10.1007/s10640-019-00389-w