Problem Set 4: Remote Sensing
International Environmental Economics (EEP 132 / Econ C184)
This problem set is out of 11 points, each subpart is worth one point.
1. This question is based on the following paper: Fowlie, Meredith, Edward Rubin, and Reed
Walker. 2019. ²ing Satellite-Based Air Quality Estimates Down to Earth. merican Economic
Association Papers and Proceedings 109: 283-288.
a. Consider Figure 1. Assuming satellite-based readings are true, which type of error (Type
I or Type II) does this graph suggest is more common, and why?
b. Again, assuming satellite-based readings are true, according to the paper, how many
people in the US are affected by Type I errors?
c. In class, we discussed 3 main advantages of remote sensing data over traditional data
collection. Which of the three are relevant in this paper?
d. Think about a regression of satellite-based estimates on monitor-based estimates. If we
are only interested in understanding how these estimates are correlated, should we worry
that this regression suffers from omitted variables bias?
e. The Biden Administration has promised to expand US involvement in global climate
change negotiations and to strengthen US climate change policy. An important aspect of
global climate change coordination is monitoring greenhouse gas emissions from each
country. Could the two satellite data products used here provide that monitoring?
2. Suppose that regulating fuel economy for heavy duty trucks (i.e., 18-wheelers that transport
goods) costs $500 million today in up-front capital costs, but decreases climate damages in the
year 2050 by 500 million, and in the year 2100 by $1.5 billion. Please show your equation and
result for the first two subparts:
a. At a discount rate of 3%, what is the NPV of this investment?
b. At a discount rate of 7%, what is the NPV of this investment?
c. Based on the NPV criterion, should society make the investments in cases (a) and (b)?
d. If you were a government analyst, what criteria besides NPV might you want to consider
in deciding whether society should make this investment?
NBER WORKING PAPER SERIES
BRINGING SATELLITE-BASED AIR QUALITY ESTIMATES DOWN TO EARTH
Meredith Fowlie
Edward A. Rubin
Reed Walker
Working Paper 25560
http://www.nber.org/papers/w25560
NATIONAL BUREAU OF ECONOMIC RESEARCH
1050 Massachusetts Avenue
Cambridge, MA 02138
February 2019
We thank both Qian Di and Aaron van Donkelaar for sharing their high-resolution, satellite-based
PM2.5 estimates. We thank Ellen Lin and Catherine Wright for extraordinarily helpful research
assistance. Financial support from the Alfred P. Sloan Foundation is gratefully acknowledged.
The views expressed herein are those of the authors and do not necessarily reflect the views of the
National Bureau of Economic Research.
NBER working papers are circulated for discussion and comment purposes. They have not been
peer-reviewed or been subject to the review by the NBER Board of Directors that accompanies
official NBER publications.
2019 by Meredith Fowlie, Edward A. Rubin, and Reed Walker. All rights reserved. Short
sections of text, not to exceed two paragraphs, may be quoted without explicit permission
provided that full credit, including .otice, is given to the source.
Bringing Satellite-Based Air Quality Estimates Down to Earth
Meredith Fowlie, Edward A. Rubin, and Reed Walker
NBER Working Paper No. 25560
February 2019
JEL No. H23,H41,Q50,Q52,Q53
ABSTRACT
We use state-of-the-art, satellite-based PM2.5 estimates to assess the extent to which the EPA’s
existing, monitor-based measurements over- or under-estimate true exposure to PM2.5 pollution.
Treating satellite-based estimates as truth implies a substantial number of “policy errors”öerregulating areas that comply with air quality standards and under-regulating other areas that
appear to violate standards. We investigate the health implications of these apparent errors and
highlight the importance of accounting for prediction error in satellite-based estimates.
Uncertainty in “policy errors” increases substantially when we account for these underlying
prediction errors.
Meredith Fowlie
Department of Agricultural & Resource Economics
University of California, Berkeley
301 Giannini Hall
Berkeley, CA 94720-3310
and NBER
fowlie@berkeley.edu
Edward A. Rubin
Department of Economics
University of Oregon
1285 University of Oregon
Eugene, OR 97403-1285
edwardr@uoregon.edu
Reed Walker
Haas School of Business
University of California, Berkeley
2220 Piedmont Ave
Berkeley, CA 94720
and NBER
rwalker@haas.berkeley.edu
1
Introduction
Particulate matter pollution poses serious health risks!rticularly for children, the elderly, and sensitive populations. In the U.S., air pollution regulations have increasingly focused on smaller particles,
such as those less than 2.5 micrometers (PM2.5 ). These regulations are enforced using ambient air
pollution measurements, collected from the EPAàair-quality monitoring network.
The network of regulatory-grade monitors is spatially sparse; more than 80 percent of U.S. counties
do not contain a PM2.5 monitor. Coarse measurements of air-pollution concentrations can lead to
significant gaps in our understanding of the burden of exposure for certain areas. These gaps have
potentially important implications for the design and implementation of existing air-quality regulations.
Recent advances in satellite technology, combined with advances in prediction techniquesng., machine learningay relax some of these information constraints. For instance, a growing suite of satellite
observations of aerosol optical depth (AOD) make it possible to estimate ground-level concentrations of
PM2.5 at fine spatial resolutions (
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