Abstract:
We investigate how external emission prices influence robustly optimal reforestation in the Brazilian Amazon and its biodiversity impacts. Extending the findings of Assunção et al. (2023), we revisit their spatial-dynamic model of land allocation under uncertainty. Their analysis reveals that financial transfers of $25 per ton of CO2e can shift the Amazon from emitting 17 gigatons of CO2e to capturing 18 gigatons over 30 years. Our study expands this work by integrating scientific insights to evaluate biodiversity outcomes, highlighting the broader ecological benefits of emission pricing as a mechanism for achieving both carbon sequestration and biodiversity conservation.
Supplementary Online Appendix
Forthcoming in the American Economic Review (AER) Papers and Proceedings
Acknowledgments:
Hansen: University of Chicago (email: lhansen@uchicago.edu); Scheinkman: Columbia University (email: js3317@columbia.edu). We thank Pengyu Chen and Patricio Hernandez Senosian for their valuable research assistance throughout this project. We are also grateful to Zhaoyang Xu for assistance in the final stages of manuscript preparation and to Diana Petrova for her excellent editorial comments and suggestions on the paper. This project was partially supported by the Haddad Fund for Economics Research at the Becker Friedman Institute for Economics at the University of Chicago.
Uncertainty, as it pertains to climate change and other policy challenges, operates through multiple channels. Such challenges are commonly framed using social valuations such as the social cost of climate change and the social value of research and development. These valuations have contributions that vary across horizons. We propose decompositions when the nature of this uncertainty is broadly conceived. By drawing on insights from decision theory, stochastic impulse response theory, and the pricing of uncertain cash flows, we provide novel characterizations. We use these methods to illustrate when and why uncertainty leads to more proactive policy approaches to climate change.
Abstract:
Some portions of land in the Brazilian Amazon are forested, and other portions are used in agricultural activities, principally cattle-ranching. Deforestation emits carbon, and reforestation captures it. Both are consequential for the global climate. The social and private productivities for the alternative land uses vary across locations within the Amazon region. In this research, we build and analyze a spatial/dynamic model of socially efficient land allocation to establish a benchmark for ad-hoc policies. We incorporate the stochastic evolution of cattle prices into our analysis, and we explore the consequences of ambiguity in the location-specific productivities on the socially efficient policy. Finally, we assess the consequences of imposing alternative social costs of carbon emissions on the spatial/dynamic allocation of land use. Our results indicate that even modest transfers per ton of net CO2 would incentivize Brazil to choose policies that produce substantial capture of greenhouse gases in the next 30 years. Our analysis points to the management of tropical forests as an important contributor to climate change mitigation in the near future.
We thank Pengyu Chen, Bin Cheng, Patricio Hernandez, João Pedro Vieira, Daniel (Samuel) Zhao for their expert research assistance and to Joanna Harris and Diana Petrova for their helpful comments.
View on SSRN
Abstract
We study asset pricing implications of a revealing and tractable formulation of smooth ambiguity investor preferences in a continuous-time environment. Investors do not observe a hidden Markov state and instead make inferences about this state using past data. We show that ambiguity about this hidden state distribution alters investor decisions and equi-librium asset prices. Our continuous-time formulation allows us to apply recursive filtering and Hamilton-Jacobi-Bellman methods to solve the modified decision problem. Using such methods, we show how characterizations of portfolio allocations and local uncertainty-return trade-offs change when investors are ambiguity-averse.
Keywords— Risk, ambiguity, robustness, asset pricing, portfolio allocation, continuous time
Related: Read Research Reflection by Hansen – “Navigating Uncertainty” March 11, 2022
View on publishers’ website
Abstract
Climate change poses an important policy challenge for governments around the world. The challenge is made all that much more difficult because of the multitude of potential policymakers involved in setting the policy worldwide. What then should be the role of central banks? How are climate change concerns similar to or distinct from those of other natural disasters? Clarity of ambition and execution will help to ensure that central banks maintain credibility. By adhering to their mandated roles, they retain their critically important distance from the political arena. Their credibility will be further enhanced by avoiding the temptation to exaggerate our understanding of climate change.
Abstract
The design and conduct of climate change policy necessarily confronts uncertainty along multiple fronts. We explore the consequences of ambiguity over various sources and configurations of models that impact how economic opportunities could be damaged in the future. We appeal to decision theory under risk, model ambiguity and misspecification concerns to provide an economically motivated approach to uncertainty quantification. We show how this approach reduces the many facets of uncertainty into a low dimensional characterization that depends on the uncertainty aversion of a decision-maker or fictitious social planner. In our computations, we take inventory of three alternative channels of uncertainty and provide a novel way to assess them. These include i) carbon dynamics that capture how carbon emissions impact atmospheric carbon in future time periods; ii) temperature dynamics that depict how atmospheric carbon alters temperature in future time periods; iii) damage functions that quantify how temperature changes diminish economic opportunities. We appeal to geoscientific modeling to quantify the first two channels. We show how these uncertainty sources interact for a social planner looking to design a prudent approach to the social pricing of carbon emissions.
View on the NBER Macroeconomics Annual – The University of Chicago Press Journal Website
Abstract:
The COVID-19 pandemic that we are experiencing is both tragic and shocking. There is no question that, except in some Asian countries trained by prior infectious outbreaks, most policy makers around the world have been ill-prepared to respond to the crisis. The effects of the coronavirus on our mental and physical health has been indeed calamitous, and the economic and financial impacts for many have been truly unfortunate. Furthermore, the extreme nature of the event is challenging researchers to compile and interpret new evidence that is arriving at a rapid pace. The editors Hui Chen, Thierry Foucault, Jeffrey Pontiff, and Nikolai Roussanov and contributing authors are to be commended for assembling and collating a thought-provoking collection of papers. More time and study will be needed to fully sift through the evidence and to glean the lessons to be learned from this pandemic for policy makers and investors. But the evidence and insights in this volume are a very good start.
All papers of this volume are posted online on the Oxford University Press website.
Abstract:
We live in a world surrounded by uncertainty. In this essay, I show that featuring this phenomenon more in economic analyses adds to our understanding of how financial markets work and how best to design prudent economic policy. This essay explores methods that allow for a broader conceptualization of uncertainty than is typical in economic investigations. These methods draw on insights from decision theory to engage in uncertainty quantification and sensitivity analysis. Uncertainty quantification in economics differs from most sciences because there is uncertainty both from the perspective of an external observer and from people and enterprises within the model. I illustrate these methods in two example economies in which the understanding of long-term growth is limited. One example looks at uncertainty ramifications for fluctuations in financial markets, and the other considers the prudent design of policy when the quantitative magnitude of climate change and its impact on economic opportunities is unknown.
View Paper in the Annual Review of Economics
View Paper on SSRN
We use decision theory to confront uncertainty that is sufficiently broad to incorporate models as approximations. “We presume the existence of a featured collection of what we call structured models that have explicit substantive motivations. The decision maker confronts uncertainty through the lens of these models, but also views these models as simplifications, and hence, as misspecified. We extend the max-min analysis under model ambiguity to incorporate the uncertainty induced by acknowledging that the models used in decision-making are simplified approximations. Formally, we provide an axiomatic rationale for a decision criterion that incorporates model misspecification concerns. We then extend our analysis beyond the max-min case allowing for a more general criterion that encompasses a Bayesian formulation.
JEL codes- C54, D81
Online Appendix
Forthcoming in the Review of Economic Studies
This research received the Best Paper Award at the 1st MUSEES Conference, as presented by my co-author, Fabio Maccheroni.
