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
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
Geophysicists examine and document the repercussions for the earth’s climate induced by alternative emission scenarios and model specifications. Using simplified approximations, they produce tractable characterizations of the associated uncertainty. Meanwhile, economists write simplified damage functions to assess uncertain feedbacks from climate change back to the economic opportunities for the macroeconomy. How can we assess both climate and emissions impacts, as well as uncertainty in the broadest sense, in social decision-making? We provide a framework for answering this question by embracing recent decision theory and tools from asset pricing, and we apply this structure with its interacting components in a revealing quantitative illustration.
Online Appendix
Associated Results and Python Scripts – GitHub
