Transcript No Slide Title

Integrated Forestry Ozone
Regulatory Modeling System
(InFORMS)
Economic Benefits of Reduced Ozone
Damage to Eastern US Forests Resulting
from US EPA’s Heavy Duty Engine/Diesel
Fuel Final Rule
& Brief Discussion of Forestry
Aesthetics
Bryan Hubbell,1 Patricia Koman,1 John Laurence,2 Brian
Heninger,3 Andrea Petro,4 John Mills,5 and Richard
Haynes 5, Yewah Lau1
Presented by Linda M. Chappell Ph.D.1
•
1US
•
2US
•
3US
•
4Indiana
•
5US
•
For More Information: www.epa.gov/otaq/diesel.htm or ww.fs.fed.us/pnw/serv/rpa/model.htm
Environmental Protection Agency, Office of Air Quality Planning and Standards, Innovative Strategies and
Economics Group, Research Triangle Park, NC 27711
Environmental Protection Agency, Office of Research and Development, NHEERL, Western Ecology
Division, Corvallis, Oregon 97330 and Boyce Thompson Institute for Plant Research, Cornell University,
Ithaca, NY, 14853
Environmental Protection Agency, National Center for Environmental Economics, 1200 Pennsylvania Ave,
Washington, DC 20460
University, School of Public and Environmental Affairs, 1315 E. 10 th Street, Bloomington, IN 47405
Forest Service, Pacific Northwest Research Station, 1221 SW Yamhill, Suite 200, Portland, OR 97208
• The US EPA finalized the Heavy Duty Engine/Diesel
Fuel rule in December 2000. The NOx emissions
reductions from this rule contribute to a constellation of
beneficial ecosystem effects related to forest health.
– We focused on commercial forest productivity
benefits of reduced ozone damage to Eastern U.S.
forests that will result from reductions in NOx
emissions when the policy is fully phased in.
• For commercial forestry, well-developed techniques are
available to estimate biological and market changes
independently; however, this is the first time we have
integrated them as we have here in the Integrated
Forestry Ozone Regulatory Modeling System
(InFORMS).
• Our modeling framework integrates
– Atmospheric Chemistry: modeled future ozone
concentrations from Urban Airshed Model (UAM-V);
– Biology: species-specific concentration-response
functions estimated from TREGRO model simulations
and USDA’s Forest Inventory Analysis data; and
– Economics: modeled by the Timber Assessment Market
Model (TAMM)/Aggregated Timberland Assessment
System (ATLAS).
• Annual benefits = sum of the annualized present value of
the stream of benefits (change in consumer and producer
surplus) over a 30 year period plus the annualized present
value of additional accumulated forest inventories.
Air Quality Inputs
Biological Inputs
Model: UAM-V
Model: TREGRO
Scope: Eastern US at county level
Scope: 6 Species in 6 Eastern regions at county level
Metric: SUM06 in 2030 for base case and HD
Engine/Diesel Fuel control scenario
Metric: Relative Stem Biomass Loss
Concentration-Response functions
County-level Growth Adjustment
Factors by Species
Multi-Stage Weighting Process
(1) Assign weights based on
(2) Aggregate county data to
county-level species-specific
TAMM/ATLAS Regions by
biomass estimates
species
(3) Aggregate species to ATLAS forest types
within TAMM/ATLAS Regions.
Economic Modeling
Model: TAMM/ATLAS
Scope: National with Eastern O3 changes
only; assumes no change in West or Canada
Metric: Net Present Value of changes in
producer and consumer surplus and value of
stumpage inventory from 2020 to 2050
Air Quality Inputs: UAM-V
US EPA’s Urban
Airshed Model
•Predicting county-level
year-round ozone
concentrations
•Eastern domain only
•In year 2030 with and without the HD Engine/ Diesel Fuel rule
•Policy fully implemented in 2030 with truck fleet turn-over
•Ozone season (May – September) using eVNA to interpolate data
Air Quality Inputs: UAM-V
Summary of UAM-V Derived Ozone Air Quality Metrics in Eastern U.S.
Due to HD Engine/Diesel Fuel Rule
Statistic a
2030 Base Case
Change b
Percent Change b
Sum06 (ppb)
Minimum c
0.00
0.00
0.00%
Maximum c
53.36
-29.10
-54.54%
Average
21.66
-16.91
-78.05%
Median
23.44
-19.50
-83.19%
a
SUM06 is defined as the cumulative sum of hourly ozone concentrations over 0.06 ppm (or 60 ppb) that occur during
daylight hours (from 8am to 8pm) in the months of May through September. It is calculated at the county level based
on the results of enhanced spatial interpolation.
The change is defined as the control case value minus the base case value. The percent change is the “Change”
divided by the “2030 Base Case,” which is then multiplied by 100 to convert the value to a percentage.
b
c
The base case minimum (maximum) is the value for the county level observation with the lowest (highest)
concentration.
Biological Inputs: TREGRO
•Using TREGRO-derived
region-specific functions
relating biomass loss to
changes in ozone in 6 species
• Black Cherry
• Loblolly Pine
• Red Oak
• Red Spruce
• Sugar Maple
• Tulip Poplar
Biological Inputs: TREGRO Zones
Multi-Stage Weighting Process
TREGRO
Function
Growth
To set up the economic model
we must know what portion of
the ATLAS forest inventory is
affected by ozone changes from
the policy (for the species and
areas we are able to quantify).
County X
ATLAS Regions and
Forest Types (e.g.,
Lowland hardwood)
Multi-Stage Weighting Process
Analytical Steps:
1. Assign weights based on
county-level species-specific
biomass estimates
2. Aggregate county data to
TAMM/ATLAS regions by
species
3. Aggregate species to
ATLAS forest types within
TAMM/ATLAS regions
Change in Growth Adjustment
Factors (x 10-6)
TAMM
Region
Black
Cherry
Loblolly
Pine
Red
Oak
Red
Spruce
Sugar
Maple
Tulip
Poplar
Plain &
Central
States
295
0
64
0
14
0
Lake
States
411
0
2
8
12
85
Northeast
702
182
5
811
38
89
South
Central
373
231
176
0
10
53
9,841
786
256
0
790
233
Southeast
Economic Modeling: TAMM/ATLAS
•TAMM evaluates timber
production and market
changes
•Spatial model of
solidwood and timber
inventory in US
•Timber price, quantity
•Net change in consumer and
producer surplus and change in
value of accumulated inventories
•Area for further research
and analysis
Research Needs for Economic Benefits Analysis
Air Quality Inputs
Biological Inputs
Western US air quality changes
Model performance in
rural/remote settings
Additional species parameterized;
extrapolations to other species*
Stand-level interactions (Zelig)
Canadian air quality changes
Western tree inventories
Multi-year modeling
Non-timber related values
Economic Modeling
Ability to model long time horizons and
sensitivities to assumptions
Comparison with Forest and
Agricultural Sector Optimization Model
(FASOM) in which long-term trends may
be changed and the Subregional Timber
Supply Model (STSM) that may be
better able to handle marginal impacts
*See next slide
Additional Species
Parameterized
• Ponderosa Pine, Red Maple, American
Basswood, Chestnut Oak, White Ash, and
White Fir
• Enhances coverage of marketable species in
the US
Forestry Aesthetics
• Air pollution can cause a range of visual injuries
to forest (discoloration of leaves to extensive
defoliation and death of trees).
• Pollutants that may cause visual forestry
symptoms include tropospheric ozone, sulfur
dioxide, hydrogen sulfide (other pollutants include
mineral acids, heavy metal such as lead and
mercury, nitrogen oxides, ammonia, peroxyacetyl
nitrate, chlorides, and ethylene).
• Evidence indicates people value forest aesthetics
and change outdoor recreational behavior
according to the quality of forest health
Limited Analysis
• Benefits & Cost of the Clean Air Act 1990 to 2010
evaluates this category of benefits as an illustrative
calculation.
• Research needs include:
– Natural science component of assessment (trends in
forest health, links between forest health and air
pollution, and dose-response relationships)
– Economic valuation studies
– Long-term monitoring networks that are capable of
linking causal agent(s) to forestry aesthetics
Conclusions
Progress has occurred in the area
of commercial forestry benefit
assessments!
Much work is required to assess
economic aesthetic forestry benefits
with any degree of specificity!