Abstract
Do trees in urban areas benefit human health? More than 100 million Americans live in large cities, yet little is known about the health benefits of the trees they live with. I provide causal evidence on the elasticity of air pollution and mortality to urban forest loss from the exogenous introduction of the emerald ash borer insect to the continental United States. Trees benefit urban health by reducing pollution; dieback that affects up to 5.8% of city forests is associated with increases in mean PM2.5 levels that reach 4.4%. Damage to city forests ultimately leads to excess deaths of up to 1.8%; much of this increase is driven by increases in cardiovascular and respiratory disease mortality. If the estimated median elasticity of all-cause mortality to tree damage of − 0.42 is extrapolated to all forests in the urban continental United States, my results imply urban forests reduced all-cause mortality by 29.3%, or 299,000 deaths total, in 2014.
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Notes
These refer to the “large central metro” counties in the NCHS (n.d.) 2013 Urban–Rural Classification Scheme for Counties.
The urban heat island effect refers to the phenomenon that urban areas experience higher temperatures than peripheral rural areas (Stone Jr. and Rodgers 2001).
Dieback is defined as “the progressive death of twigs and branches which generally starts at the tips” (Pataky 1996).
Biocontrol, which entails using natural enemies to control a pest, is viewed as the “primary tool” in managing EAB (USDA Animal and Plant Health Inspection Service 2018). Releases of the first broadly effective biocontrol agent, the parasitic wasp Spathius galinae, began in 2015 (Duan et al. 2019), after my sample period ends.
“The 2012 USDA Plant Hardiness Zone Map is the standard by which gardeners and growers can determine which plants are most likely to thrive at a location. The map is based on the average annual minimum winter temperature, divided into 10-degree F zones.” (U.S Department of Agriculture Agriculture Research Service 2012).
Emerald ash borer adults do not cause significant damage (McCullough 2020).
More precisely, the annual maximum (minimum) of mean monthly high (low).
Another possible approach would be to use emerald ash borer infestation as an instrument for the channels by which trees affect human health. I do not do so as there are multiple endogenous channels such as pollution and temperatures but only one instrument.
Estimates in levels are in Online Appendix Table A2. While showing similar patterns, they appear to be confounded by differential trends, which are not present in the log specification. It is not possible for specifications in levels and logs to simultaneously have parallel trends unless the outcomes are the same in the base year (McKenzie 2020).
Detection methods include insect traps, removing the bark from ash logs, and visually inspecting ash trees (U.S. Department of Agriculture Animal and Plant Health Inspection Service Plant Protection and Quarantine 2020).
This website is “a collaborative effort of the USDA Forest Service and Michigan State University to provide comprehensive, accurate and timely information on the emerald ash borer” (Emerald Ash Borer Information Network 2021a).
There are inconsistencies between the records of detection status and year in these two sources. I treat any county where a detection is listed in either source as detected and use the minimum of detection years.
For a concrete example of this, consider Hudson County, New Jersey. Tree death here probably started around 2013. However, my sample ends in 2014. Hudson County contributes to the coefficients up to event year 1, but not the coefficients after that. Any observed change in coefficients from event year 1 to 2 may be the result of Hudson County not being observed in event year 1 but not 2.
Suppose there is only 1 urban county in the combination of state and climatic zone. Then, the state by climatic zone and year fixed effect completely absorbs all variation from this county, and the county does not contribute to the event study coefficients.
Let A be the pollutant concentration after filtering and B be the concentration before filtering. Then the removal efficiency is given by \(\left( {B - A} \right)/B\) (Yin et al. 2007). From the elasticity we know that if we remove all vegetation (green cover decreases by 100%), pollution increases by 82%. Noting that removing all green cover implies we go from filtering (A) to no filtering (B), this means that \(B/A\)=1.82, or \(B = 1.82A\). Substitute this into \(\left( {B - A} \right)/B\) to give the removal efficiency of 45%.
Restricted to grid cells up to 100 km away.
These rather large numbers should be viewed in the context of being the result of a large treatment, since my results are extrapolated to calculate the effect of the loss of the entire urban forest.
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Tan, B.Y. Save a Tree and Save a Life: Estimating the Health Benefits of Urban Forests. Environ Resource Econ 82, 657–680 (2022). https://doi.org/10.1007/s10640-022-00677-y
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DOI: https://doi.org/10.1007/s10640-022-00677-y