The Macroeconomics of Climate Change
Paper Session
Friday, Jan. 5, 2024 2:30 PM - 4:30 PM (CST)
- Chair: Alessandro Peri, University of Colorado-Boulder
On the Geographic Implications of Carbon Taxes
Abstract
A unilateral carbon tax trades off the distortionary costs of taxation and the future gains from slowing down global warming. Because the cost is local and immediate, whereas the benefit is global and delayed, this tradeoff tends to be unfavorable to unilateral carbon taxes. We show that this logic breaks down in a world with trade and migration where economic geography is shaped by agglomeration economies and congestion forces. Using a multisector dynamic spatial integrated assessment model (S-IAM), this paper predicts that a carbon tax introduced by the European Union (EU) and rebated locally can, if not too large, increase the size of Europe’s economy by concentrating economic activity in its high-productivity non-agricultural core and by incentivizing immigration to the EU. The resulting change in the spatial distribution of economic activity improves global efficiency and welfare. A unilateral carbon tax with local rebating introduced by the US generates similar global welfare gains. Other forms of rebating can dilute or revert this positive effect.Solving Heterogeneous Agent Models with the Master Equation
Abstract
This paper proposes an analytic representation of perturbations in heterogeneous agent economieswith aggregate shocks. Treating the underlying distribution as an explicit state variable, a single
value function defined on an infinite-dimensional state space provides a fully recursive representation
of the economy: the ‘Master Equation’ introduced in the mathematics mean field games literature.
I show that analytic local perturbations of the Master Equation around steady-state deliver dramatic simplifications. The First-order Approximation to the Master Equation (FAME) reduces to a standard Bellman equation for the directional derivatives of the value function with respect to the distribution and aggregate shocks. The FAME has six main advantages: (i) finite dimension; (ii) closed-form mapping to steady-state objects; (iii) applicability when many distributional moments or prices enter individuals’ decision such as dynamic trade, urban or job ladder settings; (iv) block-recursivity bypassing further fixed points; (v) mapping to analytic sequence-space derivatives; (vi) fast implementation using standard numerical methods. I develop the Second-order Approximation to the Master Equation (SAME) and show that it shares these properties, making the approach amenable to settings such as asset pricing. I apply the method to two economies: an incomplete market model with unemployment and a wage ladder, and a discrete choice spatial model with migration.
Deep Uncertainty Quantification: With an Application to Integrated Assessment Models
Abstract
This paper presents a comprehensive method for efficiently solving stochasticIntegrated Assessment Models (IAMs) and performing parametric uncertainty
quantification. Our approach consists of two main components: a deep learning algorithm designed to globally solve IAMs as a function of endogenous and
exogenous state variables as well as uncertain parameters within a single model
evaluation. Additionally, we develop a Gaussian process-based surrogate model to
facilitate the efficient analysis of key metrics, such as the social cost of carbon, with
respect to uncertain model parameters. Our approach enables a rapid estimation
of Sobol’ indices, Shapley values, and univariate effects, which would otherwise be
computationally very challenging. To demonstrate the effectiveness of our method,
we posit a high-dimensional stochastic IAM that aligns with cutting-edge climate
science. This model incorporates a social planner with recursive preferences,
iterative belief updates of equilibrium climate sensitivity using Bayes’ rule, and
stochastic climate tipping. Our computations reveal that most of the variability in
the social cost of carbon stems from the parametric uncertainty in the equilibrium
climate sensitivity and in the damage function. We also show that the uncertainty
about the equilibrium climate sensitivity resolves in about a decade, which in turn
leads to higher optimal temperatures and a slightly decreased social cost of carbon
compared to a modeling set-up without Bayesian learning.
Discussant(s)
Elisa Belfiori
,
Torcuato Di Tella University
Alessandro Peri
,
University of Colorado-Boulder
David Childers
,
Carnegie Mellon University
Victor Duarte
,
University of Illinois-Urbana-Champaign
JEL Classifications
- Q5 - Environmental Economics
- F6 - Economic Impacts of Globalization