Integrated Forestry Ozone Regulatory Modeling System (InFORMS) Economic Benefits of Reduced Ozone Damage to Eastern US Forests Resulting from US EPA’s.

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 Environmental Protection Agency, Office of Air Quality Planning and Standards, Innovative Strategies and Economics Group, Research Triangle Park, NC 27711

• 2US 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

• 3US Environmental Protection Agency, National Center for Environmental Economics, 1200 Pennsylvania Ave, Washington, DC 20460

• 4Indiana University, School of Public and Environmental Affairs, 1315 E. 10 th Street, Bloomington, IN 47405

• 5US Forest Service, Pacific Northwest Research Station, 1221 SW Yamhill, Suite 200, Portland, OR 97208

• For More Information: www.epa.gov/otaq/diesel.htm or ww.fs.fed.us/pnw/serv/rpa/model.htm

• 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.

Model: UAM-V

Scope: Eastern US at county level

Metric: SUM06 in 2030 for base case and HD Engine/Diesel Fuel control scenario

Model: TREGRO

Scope: 6 Species in 6 Eastern regions at county level

Metric: Relative Stem Biomass Loss Concentration-Response functions

Biological Inputs

(3) Aggregate species to ATLAS forest types within TAMM/ATLAS Regions.

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

Economic Modeling

Air Quality Inputs

Multi-Stage Weighting Process

County-level Growth Adjustment Factors by Species

(1) Assign weights based on county-level species-specific biomass estimates

(2) Aggregate county data to TAMM/ATLAS Regions by species

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 duringdaylight hours (from 8am to 8pm) in the months of May through September. It is calculated at the county level basedon the results of enhanced spatial interpolation.

b 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.

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

County X

ATLAS Regions and Forest Types (e.g., Lowland hardwood)

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).

Growth

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

Southeast 9,841 786 256 0 790 233

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

Western US air quality changes

Model performance in rural/remote settings

Canadian air quality changes

Multi-year modeling

Additional species parameterized; extrapolations to other species*

Stand-level interactions (Zelig)

Western tree inventories

Non-timber related values

Biological Inputs

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

Economic Modeling

Air Quality Inputs

Research Needs for Economic Benefits Analysis

*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

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!

Conclusions