Climate Change: Impacts and Opportunities for Adaptation
Friday, Jan. 4, 2019 8:00 AM - 10:00 AM
- Chair: Antonio M. Bento, University of Southern California
Expectations and Adaptation to Environmental Risks
AbstractDespite the role that adaptation plays in determining climate change outcomes, little is known about the total adaptation potential of climate-exposed industries or the economy. Moreover, much of what is known comes from analysis of ex post adaptation to experienced weather rather than ex ante adjustments made in expectation of climate change. This paper introduces a method for estimating the value of forward-looking adaptation based on changes in expectations about the weather. The method uses changes in professional forecasts, conditional on weather realizations, to shock the beliefs of firms about future climate. The effect of those shocks on firm revenue identifies the value of forward-looking adaptation behaviors by the firm. The weather realizations, conditional on forecasts, identify direct effects of weather net of adaptation. Together, these values provide an estimate of the total effect of climate on firm welfare.
I apply the method to a novel dataset of El Niño/Southern Oscillation (ENSO) forecasts and highly disaggregated, firm-level production data to estimate adaptation by North Pacific albacore harvesters to ENSO-driven climate variation. In applying the method, I also derive theoretical conditions under which public forecasts provide good measures of private beliefs held by firms. The results show that firms are able to mitigate more than 75% of the total effect of ENSO through adaptation. Detailed data allows for exploration of mechanisms, showing that harvesters primarily adapt by choosing when to enter the fishery each season. The results illustrate both the importance of ENSO to firm welfare in this industry as well as the centrality of forward-looking adaptation to fully understanding economic consequences of changes in the climate.
A New Approach to Measuring Climate Change Impacts and Adaptation
AbstractWe propose a novel approach to estimate climate impacts and adaptation based on a decomposition of meteorological variables into long-run trends and deviations from them (weather shocks). Our estimating equation simultaneously exploits weather variation to identify the impact of shocks, and climatic variation to identify the effect of longer-run observed changes. We compare the simultaneously estimated short-and long-run effects to test for the presence and magnitude of adaptation. We apply our approach to the impact of climate change on air quality, estimating the climate penalty on ozone. Leveraging ambient ozone regulations, we find evidence of regulation-induced and residual adaptation.
Learning, Adaptation and Climate Uncertainty: Evidence from Indian Agriculture
AbstractThe profitability of many agricultural decisions depends on farmers’ abilities to predict the weather. Since climate change implies (possibly unknown) changes in the weather distribution, understanding how farmers form predictions is essential to estimating adaptation to climate change. I study how farmers learn about a weather-dependent decision, the optimal planting time, using rainfall signals. The agricultural decision I study, the timing of planting, contains information about household expectations about the monsoon, and is an economically crucial decision for farmers: a one-standard deviation from the optimal planting time in a given year can cause up to 12% lower profits in the data I use.
To capture the potential uncertainty caused by climate change, I develop an empirical framework that estimates, and finds support for, a general robust learning model in which farmers believe that the rainfall signals are drawn from a member of a set of rainfall distributions. Importantly, my empirical framework allows me to contrast the goodness-of-fit of my model to the more standard, Bayesian learning environments, and test which model fits farmers’ behavior best. Methodologically, the empirical framework I develop is quite general (and not tied to my application per se) and can be employed to test across learning models in other environments with unlearnable uncertainty.
The belief that the rainfall signals are drawn from a set of rainfall distributions rather than a single distribution are especially pronounced in villages that have experienced recent changes in rainfall distributions. This indicates that farmers respond to greater (Knightian) uncertainty in their environment by modifying their predictions to be robust to such uncertainty.
Joshua Graff Zivin,
University of California-San Diego
University of Chicago
University of Arizona
- Q5 - Environmental Economics
- I0 - General