Zack Keller
FREC480 Research Proposal
Water quality trading has emerged as means to reach efficiency in pollution abatement
markets across many different states. In this context, water quality refers to a reduction in
nitrogen and phosphorous pollution. The United States Environmental Protection Agency (EPA)
is responsible for enforcing the states setting water quality standards and the Total Maximum
Daily Loads (TMDL) which regulate these pollutants for different interstate water systems. The
TMDLs have placed responsibility in the hands of the states involved, while also giving them the
freedom to reach the necessary reductions in whatever way they feel is appropriate. The states
have pressured regulated point sources, such as waste water treatment plants, to meet stringent
reductions. Furthermore, the EPA’s TMDLs have direct consequences for the National Pollutant
Discharge Elimination System Permits Program (NPDES), which controls water pollution by
regulating the effluent discharged by those point sources.
Up till now, point sources had two options for meeting those regulations: Reduce the
amount of nitrogen and phosphorous they are discharging via plant upgrades or trade purchase
credits from other point sources that already operate within their regulated capacity. Recent
developments have presented a third option for point sources in the form of trading between
point and non-point sources. A wastewater treatment plant that discharges excess phosphorous
or nitrogen can now pay a farmer to implement practices which would achieve the necessary
reductions. The trading structure for these exchanges is developing on a state-by-state basis
because the TMDLs are set by the individual states, which are ultimately responsible for
implementing the necessary measures.
This research will focus on the Chesapeake Bay TMDL which applies to Delaware,
Maryland, New York, Pennsylvania, Virginia, West Virginia, and the District of Columbia. Of
those states, the following have developed or are in the process of developing nutrient trading
programs: Virginia, Pennsylvania, West Virginia, and Maryland. However, the regulation
underpinning these trading programs and the programs themselves seem to be in a state of flux.
The current trading structure of the Chesapeake Bay watershed allows point sources to trade only
with non-point sources located in the same basin, with some state boundaries encompassing
multiple trading basins. The GIS analysis herein will show that this is unfeasible due to the
spatial distribution of point and non-point sources.
There is a significant amount of existing literature on water quality trading which utilizes
GIS. However, the vast majority of the GIS applications in this literature focus on the
comparative attractiveness of non-point source abatement in different geographical areas.
Geographic information systems are well-equipped to evaluate the different factors that are used
to derive the benefits of abatement including the type of soil, slope of the land, and location
relative to the water system.
A top-down GIS analysis of trading structures would serve several purposes. First of all,
it would allow us to determine trading feasibility in certain basins. For example, the Everywhere
Else trading basin in Maryland encompasses a large amount of agricultural land with a relatively
small amount of significant point sources. When you further consider that all significant point
sources in Maryland are being required to implement Enhanced Nutrient Recovery (ENR)
technology, it’s easy to see how their might be an excess of credits in the short-term.
Furthermore, such an analysis might allow us to forecast relative price disparities between
trading basins at first glance. On the supply side, we could look at what the primary
crop/livestock is in each county. This would allow us to forecast the relative supply of both
phosphorous and nitrogen credits because some crops/livestock tend to be bigger polluters of one
nutrient or the other.
We will need to collect point source location (coordinate) data for all states in the
Chesapeake Bay along with their respective nitrogen and phosphorous loads. In order to figure
out what crops or livestock are most commonly produced in a given county we would probably
have to mail out surveys. If we decided to broaden this crop data out to larger regions of each
state, we might be able to find the information online. Lastly, we need to record the different
trading basins in each state.
Once all the data is collected and formatted correctly, we can begin to import it into
ArcGIS. We would then change the symbology for the point sources so that point sources with
greater loads are represented with larger symbols. County level data would then be unioned into
the different trading basins. The primary crop/livestock data could be overlaid by a raster layer
with different colors representing areas with high expected nitrogen credit supply or high
expected phosphorous credit supply.
I expect to find some trading basins that are more supply oriented and others that are
more demand oriented. This association will probably be related to the distribution of urban and
rural land in each basin because urban areas will tend to contain more significant point sources.
The policy applications of this research could potentially be far-reaching. These supply
and demand biases in certain basis might suggest that a state-level trading structure would be
more feasible, especially with short-term trading in mind. Such a structure would mitigate large
credit price disparities across basins and ultimately lower credit costs in basins which require
credits the most. Furthermore, West Virginia, Maryland, and Pennsylvania have announced that
they will allow interstate trading within basins that stretch across multiple states such as the
Potomac basin. Assuming dealers emerge in the market for credits; this agreement could lead to
credit price arbitrage, causing the credit price in these states to converge. For example, a dealer
located in the Potomac basin could buy credits in the Everywhere Else, MD basin and sell credits
in the Susquehanna basin in PA. Lastly, this research would allow us to visualize the wealth
transfer from urban to rural areas that is taking place.
The budget for this research proposal would encompass three phases: Data collection,
cleaning and formatting the data, and performing the actual GIS analysis. Data collection would
take approximately 50 hours, and it is fair to assume that one could pay an individual $15/hr to
accomplish this. Getting the data ready for use in the GIS software would take roughly 5 hours
at the same pay rate. The actual GIS analysis phase would take at least 10 hours of work.
Recognizing that the GIS work involved in this research proposal is relatively simple, a
conservative wage rate of $50/hr can be assumed. Using these workload and cost assumptions,
we would need a budget of approximately $1,325. Software costs can be ignored if the GIS
work is done using my student edition of AcrGIS. Otherwise a single ArcGIS license would
have to be purchased for $1,500, bringing the total project cost to $1,825.
Data Collection
50 hours
Clean/Format Data
5 hours
GIS
Analysis
10 hours