Economic Impacts of Climate Change

Tropical cyclones (TCs) are one of the costliest natural disasters, imparting over $50 billion in losses per year in the U.S. alone. Projected intensification of TCs in many ocean basins due to climate change, rising sea levels, and a projected continuation of coastal development will likely all contribute to increasing economic costs in coastal regions over the rest of the 21st century. However, the magnitude, spatial patterns, and dependence on emissions trajectories are all relatively uncertain. In this presentation, I will discuss two complementary models designed to help quantify the global welfare impacts of climate change on coastal communities as realized through both sea level and tropical cyclone changes. The first is a recently completed effort to dynamically model the impacts of sea level change alongside potential adaptation options, combining novel satellite-derived measures of elevation, population, and physical capital, along with sea level projections from the IPCC AR6 report. We simulate impacts and adaptation for >10,000 coastal segments worldwide across multiple emissions scenarios and probabilistic forecasts of sea level change. The second represents ongoing work to simulate the economic impact of tropical cyclone changes. In this study, we construct an open-source, physical-econometric catastrophe model that simulates high-resolution storm surge and wind from >200,000 real and synthetic storm events, probabilistically accounting for local sea level rise. We merge this hazard model with datasets defining population and physical capital both at a global scale and with a high-resolution focus in the continental U.S. Using records of losses from historical events to empirically estimate damage functions for both storm surge and wind losses, we then project future losses under different emissions scenarios. By addressing both sea level rise and tropical cyclone changes, these two models quantify complementary, yet interacting, hazards facing coastal communities. The outputs are in use in multiple contexts, from underpinning physical risk projections for financial assets to contributing to global assessments of the costs of carbon emissions. I will conclude by discussing the manner in which these models will be combined to inform a quantification of the cumulative impacts faced by coastal communities and contribute to bottom-up estimates of the Social Cost of Carbon.

Climate change threatens global food systems, but the extent to which adaptation will reduce losses remains unknown and controversial. Even within the well-studied context of US agriculture, some analyses argue that adaptation will be widespread and damages from climate change will be small, while others conclude that the scope for adaptation is limited and losses will be severe. Globally, scenario-based analyses indicate that, in principle, the extent of adaptation should have major consequences on global agricultural productivity, but there has been no systematic study of how extensively producers actually adapt in the real world at global scale. Here, we empirically estimate the net impact of producer adaptations around the world using longitudinal data on six staple crops spanning 12,658 sub-national units, capturing two-thirds of global crop calories. We project that adaptation and income growth nearly halve global losses at end-of-century, but substantial residual losses remain for all staples except rice. In contrast to analyses of other impact categories that project the greatest damages in poor regions of the world, we find that global damages are dominated by losses to modern-day breadbaskets that currently exhibit limited adaptation due to favorable climates, though losses to many poor regions are also severe. We estimate global production declines 5.8×10^14 kCal annually per 1C rise in global mean surface temperature (4.6% of current production or 130 kCal/person/day, per 1C; p

We present a unified framework for calculating country-specific climate damages resulting from past or future greenhouse gas emissions from any known emitter.  We then demonstrate four applications of this method: calculation of “carbon debt” owed by individual emitters (e.g. individual people or firms) based on past emissions, calculation of country-level “loss and damage” payments owed by large historical emitters to developing countries, correction of official GDP time series to reflect climate externalities, and construction of a damage function that maps different levels of future warming to global aggregate economic damages.

Climate risks are now being taken into account in an ever-widening range of human activities. By a “climate risk” I mean the probability of some particular loss to human society or ecosystems from climate-related hazards. Quantification of climate risks is a necessary (though by no means sufficient) condition for many kinds of action to reduce those risks. Risk is the product of hazard (the component related to climate; e.g., the probability of a given level of flooding, heat, wind, etc.), exposure (the assets at risk, which can include human or nonhuman lives), and vulnerability (the level of loss experienced for a given level of hazard). Each can be represented with different degrees of complexity, different balances of empiricism vs. first principles, and different spatial or temporal resolutions. These choices are made differently in different application areas, and it is often not clear why. What is gained or lost by doing one vs. another? How should the right approach depend on the specific problem being solved? For example, insurance industry catastrophe models, which represent individual weather events in great detail, have not (at least until recently) incorporated climate change in any serious way; while integrated assessment models (at least until recently) have not represented extreme events, nor many other consequential aspects of the climate system. My perception is that specific modeling choices are generally made for pragmatic reasons, and that there is no systematic understanding of what one gains by adding any given degree of freedom. I will argue that there is a great need for an explicit, open hierarchy of climate risk and impacts models — as exists in physical climate science — and that this presents a great intellectual opportunity. In short, we need an academic discipline of climate risk, including a theoretical dimension. I will illustrate some of these ideas with examples from recent work by my colleagues and me on tropical cyclone risk.

This is a discussion for all participants–in-person and virtual–of ways forward.

Some questions that might be addressed are: