RESEARCH

I develop and apply novel datasets and statistical methods to empirically estimate human impacts on climate and, in turn, on our food and health systems. Though my primary training is in environmental economics, I borrow extensively from other disciplines including machine learning, climate science, agronomy and epidemiology.

My research has three primary streams. In the first, I study how agricultural productivity responds to changes in climate focusing on how humans alter the transfer of sunlight through the atmosphere and how this, in turn, impacts crop yields. In the second, I explore how climate impacts the transmission of infectious disease. In the third, I develop and characterize satellite remote sensing algorithms for use in applied research. These streams converge as I develop and apply remotely sensed datasets to estimate how global environmental change affects agricultural and other socioeconomic systems.

 

Proctor, J., Hsiang, S., Burney, J., Burke, M., & Schlenker, W. (2018). “Estimating global agricultural effects of geoengineering using volcanic eruptions.” Nature, 560 (7719), 480. (here)

In this paper we leverage the major volcanic eruptions of El Chichón (1982) and Pinatubo (1991) as natural experiments to estimate the impact that solar geoengineering might have on global agricultural production. We show that changes sunlight from increased concentrations of stratospheric sulfate aerosols decrease both C4 (maize) and C3 (soy, rice, and wheat) yields. By applying our empirical yield model to earth system model simulations, we demonstrate that solar geoengineering damages from reduced sunlight wash out the technology’s benefits from cooling. A key scientific contribution of the paper is to provide the first global empirical test of the “diffuse fertilization effect” hypothesis for crop yield. We find that this theorized benefit of aerosol scattering to crop yields – namely increased photosynthetic efficiency due to a more even distribution of light throughout the canopy – was more than offset by the reduction in total sunlight. This informs not only geoengineering policy, but also regulation of other anthropogenic influences on the global optical environment such as greenhouse gas or particulate matter emissions.

 

Carleton, T., Cornetet, J., Huybers, P., Meng, K. & Proctor, J. (2021) “Evidence for Ultraviolet Radiation Decreasing COVID-19 Growth Rates: Global Estimates and Seasonal Implications.” PNAS  (here)

With boreal winter approaching, there is a need to understand how local environmental conditions may modify COVID-19 transmission. In this paper we combine a spatially-resolved dataset of confirmed COVID-19 cases, composed of 3,235 regions across 173 countries, with local environmental conditions and a distributed lag panel regression model. The central scientific contribution of this work is to quantify the individual influences of UV, temperature, humidity and precipitation on the growth rate of COVID-19 accounting for lagged effects and potential confounders. We find that a standard deviation increase in UV lowers the daily growth rate of COVID-19 cases by ∼1 percentage point over the subsequent 2.5 weeks, relative to an average in-sample growth rate of 13 percent.  The time pattern of lagged effects peaks 9-11 days after UV exposure, consistent with the combined timescale of incubation, testing, and reporting. Simulations illustrate how seasonal changes in UV have influenced regional patterns of COVID-19 growth rates from January to June, indicating that UV has a substantially smaller effect on the spread of the disease than social distancing policies.

 

Proctor, J. (2021). “Atmospheric opacity has a nonlinear effect on global crop yields." Nature Food (here and here)

Theory suggests that crop yields may have a concave response to changes in light from an increasingly opaque atmosphere, yet such an effect has never been empirically estimated. In this paper I leverage year-to-year variation in growing season cloud optical thickness to provide the first non-linear empirical estimates of how increased atmospheric opacity alters the quantity and quality of sunlight across the Earth’s surface and how this, in turn, affects maize and soy yields in the United States, Europe, Brazil and China. The central contribution of this paper is to show that the response of yield to changes in sunlight from cloud scattering and absorption is consistently concave across crops and regions, and that this response is driven by a concave response of yields to total insolation as well as by benefits from increased diffuse light in some regions. I also find that the benefits of low and moderate opacity cannot be explained by the diffuse fertilization effect alone. Rather, other benefits from moderated insolation must play a central role, such as reduced leaf temperature, increased water use efficiency or reduced photoinhibition. Globally, these results suggest that agricultural productivity is limited as much by light as by temperature. Applying these empirical estimates to earth system model simulations of air pollution and climate change, I find that anthropogenic changes in the quantity and quality of sunlight due to changes in clouds have had, and may increasingly have, economically substantial impacts on yields.

 

Rolf, E., Proctor, J., Carleton, T., Bolliger, I., Shankar, V., Ishihara, M., Recht, B. & Hsiang, S. (2021) “A Generalizable and Accessible Approach to Machine Learning with Global Satellite Imagery.” Nature Communications (here)

Combining satellite imagery with machine learning (SIML) has the potential to address global challenges by remotely estimating socioeconomic and environmental conditions in data-poor regions, yet the resource requirements of SIML limit its accessibility and use. The central contribution of this paper is to demonstrate for the first time that a relatively simple unsupervised featurization can achieve performance across many tasks (e.g., forest cover, house price, road length…etc.) and at global scale competitive with more complex neural network-based algorithms. Our method achieves accuracy competitive with deep neural networks at orders of magnitude lower computational cost, scales globally, delivers super-resolution predictions, and facilitates characterizations of uncertainty. Since image features are shared across tasks, they can be centrally computed and distributed to unlimited researchers, who need only fit a linear regression to their own ground truth data in order to achieve state-of-the-art SIML performance. This reduces the challenge of extracting information from unstructured satellite imagery to a linear regression problem using pre-computed features. We are currently producing and publicly distributing a dense featurization of the globe. We seek to enable a greater number and diversity of researchers to make state-of-the-art SIML predictions by lowering the computational and intellectual costs required to do so.