p.1 

OAR Box 1828 

Prepped by Ollie Stewart 

Document Number: 

304) IV-B-8 

Docket Number: 

A-83-27 

Printed 11/12/2008 11:12:59 AM Sheet 304 of 325 
P.2 

A-83-27 

IV-B-8 

^'°>, UNITED STATES ENVIRONMENTAL PROTECTION AGENCY 

Office of Air Quality Planning and Standards 

/ Research Triangle Park, North Carolina 27711 

Apri l 23, 1987 

MEMORANDUM 

SUBJECT: Industrial Boiler NSPS RIA - Calculation of Visibility Benefits 

FROM: Janet ScheidL ,y 7. c^^c-/d. r\(L 

EAB, SASD (MD-12) " '' 

TO: Walt Stevenson 

SDB, ESED (MD-13) 

The purpose of this memo is to detail the calculation procedures 

that were used in analyzing the benefits of visibility improvement due to 

the Industrial Boiler New Source Performance Standard. A study by Tolley, 

et. al (1986) was originally used in the draft Regulatory Impact Analysis 

as the basis for the visibility calculation. Some concerns were raised 

within and outside the EPA regarding the Tolley study and therefore I 

reexamined the decision to rely soley on this study. 

Upon further analysis, I have decided to use a group of five studies, 

rather than relying fully on just one study to calculate local visibility 

benefits. Nonlocal visibility benefits have not been calculated. The 

studies by Brookshire et al.1, Rae2, Rowe et al.3, Loehmann et al.4, and 

1 Brookshire, D., d'Arge, R., Schulze, W., and Thayer, M., (1979). 

Methods Development for Assessing Air Pollution Control Benefits. 

Vol. 2: Experiments in Valuing Non-Market Goods: A Case Study of 

Alternative Benefit Measures of Air Pollultion in the South Coast Air 

Basin of Southern California, EPA-600/6-79-0016. 

2 Rae, D., (1983). Benefits of Visual Air Quality in Cincinnati: Results 

of a Contingent Ranking Survey. Presented at the American Economic 

Association Meetings, Washington, DC. 

3 Rowe, R.D., d'Arge, R., and Brookshire, D., (1980). An experiment on the 

economic value of visibility. Journal of Environmental Economics and 

Management. March: 1-19. 

4 Loehman, E., Boldt, D. and Chaikin, K., (1981). Measuring the Benefits of 

Air Quality Improvements in the San Francisco Bay Area, SRI 8962. 
P.3 

Tolley et al.5 are chosen for this analysis. They cover ten cities and all 

use contingent'valuation surveys. The ten cities are: Chicago; Atlanta; 

Boston; Cincinnati (analyzed by Tolley and Rae); Miami; Mobile; Washington, 

DC; Los Angeles; San Francisco; and Farmington, New Mexico. The average 

annual household values from each of the five studies, for each hypothesized 

change in visual range due to the alternative standards analyzed, were 

compiled to estimate Equation 1. 

Benefits = [(b/a)(VR2-VRl)+(c/a)(VR22-VRl2] f(x) 1 

This equation suggests that the bids from the surveys can be expected to be 

a function of the change in visual range considered and of the base level 

(VR1) and new level (VR2) of visual range hypothesized. The function f(x) 

was presumed to be held constant across the different studies. The variables 

used in this analysis are defined in Table 1 and the entire data set used 

is presented in Table 2. 

Four dummy variables were defined for study characteristics that might 

influence the value estimate. These are RANK, DIST, PRETEST, and WEST and 

are defined in Table 1. 

Table 3 shows the OLS estimates of Equation 1. Both DVR and DIFSQ are 

significant. The negative coefficient for DIFSQ combined with the positive 

coefficient for DVR indicates that over the range of visibility considered 

in these studies, the value increases with bigger changes in visual range, 

but at a decreasing rate. 

To test the sensitivity of these results to the Tolley et al. estimates, 

the equation was estimated without the Tolley et al. estimates. The results 

are shown in Table 4. Estimated without the Tolley results the coefficients 

are practically identical. With the smaller sample size and the change in 

the group of studies, the dummy variables were not expected to be stable 

and were therefore left out of both estimations. 

This is strong support that the Tolley et. al. results are generally 

consistent with those of the other contingent valuation studies that have 

been conducted. The SO4 local visibility and PM morbidity and soiling 

benefit results are presented in Table 5. Two regulatory alternatives are 

presented, low sulfur fuel and percent reduction. Each alternative has a 

range of benefits which reflects sensitivity analysis on the air quality 

modeling and economic valuation inputs. The ASTRAP air quality model in 

comparison to the Monte Carlo model, yields low absolute sulfate concentration 

changes but high percentage sulfate concentration changes between the 

baseline and regulatory alternative air quality levels. For these reasons 

Tolley, G., Randall, A., Bloomquist, G., Fabian, R., Fishelson, G., 

Frankel, A., Hoehn, J., Krumm, R., Mensah, E., and Smith T., (1986). 

Establishing and Valuing the Effects of Improved Visibility in Eastern 

United States, Office of Research and Development, Environmental Protection 

Agency. 
P.4 

ASTRAP is the model used to calculate the low PM estimate and the high 

visibility estimate in Table 5. Monte Carlo yields high sulfate concentration 

changes yet low percentage changes and is therefore used to calculate the 

high PM estimate and the low visibility estimate shown in Table 5. The two 

models are averaged to obtain the middle estimates. 

The study used to calculate PM morbidity estimates presented in Table 

5 is Ostro 19866. The morbidity measure used by Ostro in his 1986 study is 

limited to only acute respiratory conditions and only uses fine particulates 

(FP) as the measure of exposure. FP, rather than TSP, is probably the most 

appropriate indicator of PM exposure since small particles are most damaging 

to health. The Ostro analysis has found consistent associations between PM 

and morbidity measures for adults that are reasonably consistent between 

and within American cities. 

cc: Allen Basala 

Fred Porter 

6 Ostro, B. D. (1987). Air Pollution and Morbidity Revisited: A 

Specification Test. Journal of Environmental Economics and Management, 

March. 
P.5 

TABLE 1 

VARIABLES USED IN ANALYSIS OF RESULTS FROM CONTINGENT VALUATION STUDIES 

Variable Description Mean 

CITY 

BID 

VR1 

VR2 

DVR 

PERHILE 

DIFSQ 

PERCENT 

LOGRAT 

RANK 

DIST 

PRETEST 

VEST 

Cities vhere the means vere estiaated—entered 

separately if aore than one city covered in one 

study. Value codes as follovs (date of survey): 

1 - Chicago - Tolley et al. (1981) 

2 - Atlanta - Tolley et al. (1982) 

3 - Boston - Tolley et al. (1982) 

4 - Cincinnati - Tolley et al. (1982) 

5 - Hiaai - Tolley et al. (1982) 

6 - Mobile - Tolley et al. (1982) 

7 - Washington DC - Tolley et al. (1982) 

8 • Los Angeles - Brookshire et al. (1978) 

9 > San Francisco - Loehoan et al. (1980) 

10 « Cincinnati - Rae et al. (1982) 

11 - Faraington, Nev Mexico - Rove et al. (1977) 

Annual villingness to pay per household in 1984 100.47 

dollars. Mid points of ranges vere used for Rae 

study. Foroat C2 (payment card vith private and 

public goods) vas used for Tolley et al. pretest. 

Visual range in miles that vas, the presumed 14.98 

starting point for the hypothesized change 

Visual range in miles that vas the hypothesized nev 19.48 

level 

Change in visual range hypothesized (VR2 - VR1) 4.50 

BID/DVR 29.64 

[(VR2)2 - (VR1)2] -4.74 

DVR/VR .82 

L0G(VR2/VR1) .34 

1 • contingent ranking method vas used. .17 

1 • visual range vas presented as a distribution of .17 

several levels* not a single average value. 

1 • study yas a pretest for a larger effort. .19 

1 a study conducted in the Western U.S. .19 
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TABLE 3 

REGRESSION ESTIMATES OF EQUATION 1 FOR ALL CONTINGENT VALUATION STUDIES 

WITH DUMMY VARIABLES FOR DIFFERENCES IN STUDIES 

Variable Coefficient t-statistic 

DVR 19.74 5.62 

DIFSQ -.17 -3.11 

RANK 258.15 3.28 

DIST -91.41 -1.14 

PRETEST -5.23 -.10 

WEST -6.39 -.09 

H2 - 36 

R - .77 

F - 16.33 
P.8 

Table 4 

Regression Estimates of Equation 1 for Al l CV Studies 

N2 = 15 

R = .50 

F = 6.46 

Al l Studies 

Variable Coefficient t-statisti c 

DVR 22.31 6.75 

DIFSQ -.19 -4.20 

N2 = 36 

R = .64 

F = 29.9 

All Studies Except Tolley et al 

Variable Coefficient t-statistic 

DVR 22.37 3.20 

DIFSQ -.19 -2.42 
P.9 

Table 5 

Annualized Benefits1 

(thousands of 1983 dollars)2 

Regulatory Alternatives

 3 

Industrial Boilers SO2 

Benefit Category 

S 04 Visibility 

PM Morbidity & Soiling 

1 

19,000 

22,000 

Low Sulfur 

2 

26,000 

25,000 

Fuel 

3 

36,000 

27,000 

Percent Reduction 

1 2 3 

29,000 

34,000 

38,000 

37,000 

52,000 

40,000 

1 The benefit assessment only includes a subset of potential benefits. 

2 Benefits are for the year 1990, in 1983 dollars. 

3 A range of benefits (1,2,3) is presented to reflect sensitivity analysis on 

the air quality modeling and economic valuation inputs.