## Regulatory Impact Analysis of the Standards of

Perform ance for Stationary Com pression Ignition Internal Com bustion Eng inesEPA-452/R-06-003 June 2006



### Regulatory Impact Analysis of t he Standards of Performance for Stationary Com pression Ignition Internal Com bustion En gines

U.S. Environmental Protection Agency Office of Air Quality

Planning and Standards Air Benefits and Costs Group Research Triangle Park, NC 27711iii

CONTENTS

Section Page 1 Introduction

.................................................... 1-1 1.1 Executive Summary

........................................ 1-2 1.2 Organization of this Report

.................................. 1-3 2 Indu stry

Profile

.................................................. 2-1 2.1 The Supply Side

........................................... 2-1 2.1.1 Materials and Other Costs of Producing Equipment

......... 2-1 2.1.1.1 Generator Sets and Welding Equipment

........... 2-1 2.1.1.2 Pumps and Compressors

....................... 2-3 2.1.1.3 Irr

igation Syst ems ............................ 2-5 2.1.2 Potential Change s in Mater ial I nputs
..................... 2-5 2.2 The Demand Side

.......................................... 2-6 2.2.1 Generators and Welding Equipment

..................... 2-6 2.2.2 Stationary Pumps and Compressor Equipment

............. 2-8 2.2.3 Irrigation

........................................... 2-8 2.2.4 Empirical Data Elasticities

............................. 2-9 2.3 Industry

Organization

...................................... 2-10 2.3.1 Diesel Engines: The Equipment Firm’s “Make” or “Buy” Decision

.................................... 2-10 2.3.2 Defining the Products that Constitute the Market

.......... 2-11 2.3.3 Key

Firms Cur

rently Participa ting in these Mar

kets

........ 2-11 2.3.3.1 Generators and Welders

...................... 2-11 2.3.3.2 Pumps and Compressors

...................... 2-11 2.3.3.3 Irrigation Equipment

......................... 2-12 2.3.3.4 Diesel Engines

............................. 2-13 2.3.4 Description of Sma ll and L arge F irms

................... 2-13 2.3.5 Pricing

Behavi or in Equipment Mark ets
................. 2-14iv 2.4 Market D ata
............................................. 2-14 2.4.1 Baseline Quantities

................................. 2-15 2.4.2 Baseline Prices

..................................... 2-16 2.4.3 Historic al Data

..................................... 2-16 2.4.4 Projections

........................................ 2-17 3 Regulatory

Program Cost Estimates

................................. 3-1 4 Economic

Impa ct Analy sis: Methods and Resu

lts

....................... 4-1 4.1 Analytical Approa ch

....................................... 4-1 4.2 Diesel Eq

uipment Ma

rkets Af fected by t he CI NSPS

.............. 4-4 4.3 Overview of Partial Equilibrium Model

........................ 4-5 4.3.1 Market Su

pply

...................................... 4-5 4.3.2 Market Demand

..................................... 4-8 4.3.3 Equilibrium Solution

................................. 4-9 4.4 Results

.................................................. 4-9 5 Small Busi

ness I mpact Ana

lys is

.................................... 5-1 5.1 Description of Small Entities Affected

......................... 5-2 5.2 Small Busi

ness Scre ening A

naly sis

............................ 5-2 5.3 Assessment

............................................... 5-5 6 Benef

its Analy sis

............................................... 6-1 6.1 Calculat

ion of Huma

n Health B enefits

......................... 6-1 6.2 Characterization of Unce rtainty in the Be nefits Analy sis

........... 6-4 6.3 General Approach

......................................... 6-7 6.4 Monte-Ca

rlo Base d Uncer

tainty Analysis

....................... 6-8 6.5 Results of

the CAI R RIA M onte Carl o Analy sis

................. 6-10 6.6 Benefits by

Engine Size Category

............................ 6-12v 6.7 Benefit-Cost Comparison

................................... 6-15 References

........................................................... R-1vi

LIST OF F IGURES

Number Page

4-1 Derived Demand for Equipment from the Construction I ndustry

........... 4-3 6-1 Nonroad

Engine in the U.S. by St

ate
................................. 6-2vii

LIST OF TABLE S

Number Page

2-1 Motor and Generator Manufacturing : 1997 to 2002 ($billion)

............. 2-2 2-2 Welding and Soldering Equipment Manufacturing: 1997 to 2002 ($billion)

. . 2-3 2-3 Pumps and Pumping Equipment Manufacturing: 1997 to 2002 ($billion)

.... 2-4 2-4 Air and Gas Compressor Manufacturing: 1997 to 2002 ($billion)

.......... 2-4 2-5 Farm Machinery

and Equipment Manufacturing: 1997 to 2002 ($billion)

.... 2-5 2-6 Generator Set and Welding Equipment Use by Industry : 1997

............. 2-7 2-7 Pumps and Compressor Equipment Use by I ndustry: 1997

................ 2-9 2-8 Empirical Demand Elasticity Estimates: F inal Product Markets Where Stationary Diesel Equipment is Used

................................ 2-10 2-9 Firm Market Shares by Equipment Market: 2000

...................... 2-12 2-10 Baseline Quantities for Engines and Equipment: 2015

.................. 2-15 2-11 Baseline Equipment Prices: 2015

.................................. 2-16 2-12 Historical Unit Sales Data by Ma rket: 1990–2000

..................... 2-17 2-13 Projected Annual Unit Sales for Nonemergenc y CI Engines: Selected Years

. 2-18

3-1 Summary

of Total Co sts Associ ated with the NSPS

..................... 3-1

4-1 Markets Included in Economic I mpact Model

.......................... 4-6 4-2a Baseline Data: Nonemerge ncy Stationary

Diesel Generator Sets and Welders, 2015

.................................................. 4-7 4-2b Baseline Data: Nonemerge ncy Stationary

Diesel Pumps and Compressors, 2015

............................................... 4-7viii

4-2c Baseline Data: Nonemerge ncy Stationary

Diesel Irrig ation Systems, 2015

... 4-7 4-3 Summary of Economic I mpacts: 2015

............................... 4-10 4-4 Detailed Results for Generator and Welder Equipment: 2015

............ 4-11 4-5 Detailed Results for Pump and Compressor Equipment: 2015

............ 4-12 4-6 Detailed Results for Irrig ation Equipment: 2015

....................... 4-12 4-7 Shift of Diesel Fuel Supply and De mand Quantities in 2015 (billion g allons)

................................................ 4-13

5-1 Summary

Statistics for SBREFA Scre ening A naly sis

.................... 5-4 6-1 Estimate of Monetized Benefits in 2015 ($2000) Presuming CI Engines are Spatially Distributed Similar to Nonroad Engines

....................... 6-4 6-2 Res ul ts of M ont e Ca rlo Unc ert ain ty As ses sm ent fro m C AIR R IA

......... 6-10 6-3 Estimated Monetized Benefits Compared to Two Approaches for Estimating the Un certai nty Range

........................................... 6-12 6-4 Benefits of Emission Reductions in 2015 by Eng ine Size Category

........ 6-141-1 SECTION 1 INTRODUC TION
The Sector Policies and Programs Division (SPPD) of the U.S. Environmental Protection Agenc

y’ s (EPA’s)Office

of Air Qu

ality Planning and Stand

ards (OAQPS) is developing a final rule to implement New Source Perf ormance Standards (NSPS) on compres sion igni

tion (CI ) stationa ry interna l combustion engines by

June 28, 200 6. This rule, which is in response to a consent decree a nd is under the authority of Sec tion 111(b) of the Clean Air Act, will address emissions for nitrogen oxides (NO x ), sulfur dioxide (SO 2 ), particulate matter (PM), nonmethane hy drocarbons (NMHC), and carbon monoxide (CO) from new CI engines. The requirements of the NSPS generally follow the nonroad diesel engine rule developed by

EPA’s Office of Transportation and Air Quality (OTAQ) in 2004. The NSPS contains re quireme nts for ow

ners, ope rators, and manuf

acture rs of stat ionary CI engines. The NSPS requires manufacturers to certify their 2007 a nd later model yea

r stationary nonemer

gency CI engines to the Tier 2, T ier 3, and Tier 4 certification emission standar ds for non

road die sel engines for

all the po llutants ( except SO 2 ). This NSPS incorporates most of the requirements of the final nonroad e ngine rule. All new stationary CI engines orde

red after the proposal date of this rule (June 29, 2005) and manuf actured after April 1, 2006 (or July 1, 2006, for fire pump eng ines) will be covered. Engine manufac turers must follow the certification procedures and warr anty, maintenanc e, installation, and labeling requirements specified in the nonroad engine rule. Only ce rtain new engines will have to put on controls in response to this NSPS. Emergency engines have to certify

to the Tier 2 and Tier 3 sta ndards. Nonemergency

engine s with engine displa cement g reater than or e qual to 10 liters per cylinder

and less than 30 liters per cy linder displacement have to certify to the Tier 2 sta ndards f or marine engine s, and en

gines w ith displac ement g

reater than or e qual to 30 liters per cylinder

do not have to be certified by engine manufacturers; the owne rs and/or operators of eng

ines with d

isplace ment gr eater than or equ

al to 30 lite

rs per cylin

der have to meet NO x and PM limits. All other engines must be certified to the final Tier 4 emission standar ds for al l pollutant s. Such en gines a re here after r

eferr ed to as “ subject stationar y CI engines.”1-2 To suppor

t EPA’s de

velopmen t of these standar ds, EPA’s A

ir Bene fits and Co sts Group (ABCG) has conducted an ec onomic impact analysis (EI A) to assess the potential costs of the rule. This report documents the methods and results of this EI A. 1.1 Executive Summary EPA estima

tes the NSP S will resu

lt in signi ficant in

crease s in marke

t prices b ut small reducti ons in outpu

t of diese l-power

ed equipment using

the aff ected e ngines. The economic approach and engineer

ing cost approach y

ield approximately the same estimate of the total change in surplus ($57 million). However, the economic a pproach illustrates how costs flow th rou gh t he e con om ic s yste m an d i den ti fie s i mp ort ant tra nsi to ry im pac ts on s tak eho ld ers . In addition, it identifies the distribution of welfare losses across affected mar kets. The key results of the RI A are a s follows:

C Engineering Costs (2002$) : Total annualized costs measure the costs incurred by affected industries annually . The average annua lized costs for the rule totaled approximately $57.1 million.

C Price and

Quantity I

mpacts : The price impacts are significant; howeve r, demand responses to price changes ar e estimated to be small.

– The average price s for affected equipment are proje cted to increase between 2 and 10 percent. Generator set and welding equipment markets experience the highest relative change in ba seline price (9.4 percent).

– Production/consumption remain essentially unchanged, declining by less than 0.5 percent. The analysis shows that demand responses to price changes a re small because the elasticities of demand for the final produc ts or services that use aff ected e quipment a

nd the cost share o f equipme nt in produ

ction of these goods and services are small.

C Small Businesses : EPA perf ormed a s creeni

ng ana lys is for impa

cts on sma

ll businesses by comparing

compliance costs to baseline company revenues. When we compare compliance costs (costs of controls, testing , and monitoring) to total company re

venue, the ratio of compliance cost to company revenue falls below 1 percent for 57 of the 60 small companies included in the scre ening analysis. Two small businesses have costs between 1 and 3 percent of c ompany sales, and one small company has costs excee ding 3 percent of company sales. Assuming that these sma ll business es or new

businesse s like them

will be af

fected by t he NSPS, we do not believe that the CI NSPS will have a signific ant impact on a substantial number of

small enti

ties.

C Social Costs (2002$) : The eco nomic mode l estimate s a total so cial cost of the ru le of $57.0 mi llion, incl

uding $2 1.0 million o f recor dkeeping and repo rting c osts1-3 borne largely

by emergency e quipment owners to record hours of nonemerg ency operati on. Equipment produ cers pa ss on esse

ntially all costs o f contro l to downstream markets and consumers. Total consumer surplus change is –$37.6 million. Three-quarters of these losses occur in the g enerator set and welding equipmen t markets

.

C Energy U

se Impac ts : The NSPS wi ll reduc e emission s of SO 2 by r equiring the use of ultra-low sulfur diesel (ULSD) fuel. Use of low-sulfur fuel is required beginn ing in 200 7, and in the baseli ne y ear of a naly sis (2015) , new sta

tionary CI equipmen t subject t o the NSPS wi ll be req

uired to switch to U

LSD f uel (i.e ., must consume f

uel meeti ng a 15 p pm sulfur standard

). As a re sult, dema

nd for UL SD fuel will increase and demand for high- sulfur No. 2 distillate will decline. Based on revie w of fue l consumpt

ion data f rom the nonroad ru

le, the size

of the shi ft in quantitie s betwee

n these ma

rkets in 2 015 is ver

y small, and EPA anticip ates this change will have negligible influence on diesel fuel prices and producti on/consum

ption choi ces.

C Benefits : The NSPS will yield benefits of almost $1.4 billion in 2015. This is due the emissi on reductions prima rily of direct PM2.5, but t

he reduc tions in SO 2 and NOx also contribute to this total. These benefits exceed the costs by more than 20 to 1.

1.2 Organization of this Report The remainder of this report supports and details the methodology and the results of the RIA:

C Se ct io n 2 pr es en ts a p ro fi le of th e a ff ec te d i nd us tr y.

C Section 3 describes the estimated costs of the regulation.

C Section 4 d

escribe s the EI

A methodo logy

and repo rts marke t welfar e impacts

.

C Section 5 presents

estimate d impacts on small com panies.

C Section 6 present the benefits methodology and reports the results from apply ing th is me th od ol og y.2-1 SECTION 2 INDUSTRY

PROF ILE

2.1 The Supply Side In thi s industry

profile, we discu ss relate d supply -side issu es assoc iated wit

h industrie s that manu

factur e equipme

nt power ed by diesel e ngines a ffecte d by the NSPS. These industries provide three broad services: powe r (generator sets and we lding equipment), pumping and compression, and irrigation. I n this section, we discuss two important supply-side issues: costs of equipment production and tec hnologies associated with emissi on contro ls. 2.1.1 Materials a

nd Other Cost

s of Produc

ing Equipment The U.S. E conomic Ce

nsus data provide production cost dat a by industry . Beca

use industry definitions are so

broad, the data a

re limited in their a bility to provide

insight in

to absolute expenditures levels; however, the statistics provide a reasonable pr oxy of the relative importance of inputs in the manufacturing proc ess. As discussed below, all of the industrie s have sim

ilar distr ibutions of

producti on costs a

cross ma terials, labor, a

nd capita l. Diesel e ngine c osts are

approxima

tely 1 to 2 perc ent of pr

oduct val ue in thes e industr

ies. 2.1.1.1 Generator Sets and Welding Equipment The U.S. Economic Census classifies generator se ts under “Motor and Generator Manufac turing” (North A merican Indu stry Classific ation Sy

stem [NAICS] 33512). T

his industry comprises establishments primarily engaged in manufacturing electric motors (except internal combustion engine starting motors), power generators (except battery charging alternators f

or internal combustion engines), and motor gene rator sets (except turbine generator set units). I t also includes establishments rewinding armatures on a fac tory basis.2-2 As shown in Table 2-1, the variable production costs include materials (including energy ), and labor. Of these categ ories, materials (including fuel and energ y) repr esent about half of the total product value. Within the materials category , diesel and semidiesel engines account for approximately 2 percent of product value in 2002. L abor expenditures account for approximately 13 perc ent, and other costs such as capital, transportation, marketing , and markup r eprese nt the re maining 40 perce nt. Table 2-1.Motor an d Gener ator Man ufacturing: 1997 to 20 02 ($billion)

Year Value of

Shipments Cost of

M ater ials Co st as a

Share of Product Va l ue ( %) Labor Co st as a Share of Product Va l ue ( %) Capital Co st as a Share of Product Va l ue ( %) 2002 $9.1 $4.3 47% $1.2 13% $0.2 2% 2001 $9.4 $4.5 48% $1.2 13% $0.2 2% 2000 $10 .0 $4.9 49% $1.3 13% $0.2 2% 1999 $10 .8 $5.4 50% $1.4 13% $0.3 3% 1998 $11 .6 $5.7 49% $1.5 13% $0.4 3% 1997 $12 .2 $6.0 49% $1.5 12% $0.3 2% Source: U.S. Bureau of the Censu s. 2004b. “Motor and Generat or Manufactur ing: 2002.” 200 2 E con om ic

Census Manufacturing Indust ry Seri es . EC02-31 1-33531 2(RV ). Washington, DC: U.S. Bureau o f the Census . Table 1.
The U.S. Economic Census classifies welding equipment under “Welding and soldering equipment manufacturing” ( NAICS 333992). This U.S. industry comprises establishments primarily eng aged in manufacturing we lding and soldering equipment and access ories (except tran sformers), such as arc, r esistanc e, gas , plasma, laser, e lectron beam, and ultrasonic welding equipment; welding electrodes; coated or cored welding wire ; and soldering equipment (except handheld). As shown in Table 2-2, the variable production costs include materials (including energy ) and labor. Of these categ ories, materials (including fuel and energ y) repr esent about 50 to 57 percent of the total product value. Within the materials categor y, the Census did not report diesel and semidiesel engine costs. Labor expenditures account for approximately 11 percent, and other costs such as capital, transporta tion, marketing, and markup represent the remainin g 40 per cent.2-3

Table 2-2.Welding and Sol dering Equipm ent Manufacturing: 19 97 to 2002 ($billion)

Year Value of

Shipments Cost of

M ater ials Co st as a

Share of Product Va l ue ( %) Labor Co st as a Share of Product Va l ue ( %) Capital Co st as a Share of Product Va l ue ( %) 2002 $3.8 $1.9 50% $0.4 11% $0.1 3% 2001 $3.9 $2.1 54% $0.4 10% $0.1 3% 2000 $4.2 $2.3 55% $0.4 10% $0.1 2% 1999 $4.2 $2.3 55% $0.4 9% $0.1 2% 1998 $4.3 $2.3 53% $0.4 9% $0.1 2% 1997 $4.4 $2.5 57% $0.5 11% $0.1 2% Source: U.S. Bureau of the Censu s. 2004d. “Welding and Solderi ng Equipment Manufac turi ng: 2002.” 2002 Econom ic Census Manufacturing Industry Ser ies . EC02-311- 333992(RV). Washi ngt on, DC: U.S. Bureau of

the Censu s. Table 1.

2.1.1.2 Pumps and

Compresso rs The U.S. E conomic Ce

nsus clas sifies pum

ps and pump ing equ

ipment und

er “Pump and pumping equipment manufacturing” (N AICS 333911). This U.S. industry comprises establishments primarily eng aged in manufacturing general purpose pumps and pumping equipment (except fluid power pumps and motors), such as reciprocating pumps, turbine pumps, ce

ntrifug al pumps, r

otary pumps, dia

phrag m pumps, domestic wa

ter system pumps, oil well and oil field pumps and sump pumps. As shown in Table 2-3, the variable production costs include materials (including energy ), and labor. Of these categ ories, materials (including fuel and energ y) repr esent about half of the total product value. Within the materials category , diesel and semidiesel engines accounted for approximately 0.5 percent of product value in 2002. L abor expenditures account for approximately 9 percent, and other costs such as capital, transporta tion, marketin g, and markup re

present the rema ining 43 p ercent
. The U.S. Economic Census classifies compressors under “Air and gas compressor manufacturing”

(NAI CS 333912). This U.S. in dustry comprise s establishments pr imarily engaged in manufac

turing general purpose a

ir and gas compressors, such as rec iprocating compressors, centrifugal compressors, va cuum pumps (except laboratory ), and nonagricultural spray

ing and dusting compressors and spray gun units.2-4

Table 2-3.Pumps a nd Pumpin g Equipment Manufactur

ing: 1997 to 2002 ($bi

llion)

Year Value of

Shipments Cost of

M ater ials Co st as a

Share of Product Va l ue ( %) Labor Co st as a Share of Product Va l ue ( %) Capital Co st as a Share of Product Va l ue ( %) 2002 $7.0 $3.4 49% $0.6 9% $0.2 3% 2001 $7.4 $3.6 49% $0.6 8% $0.2 3% 2000 $7.6 $3.7 49% $0.6 8% $0.2 3% 1999 $7.2 $3.5 49% $0.6 8% $0.2 3% 1998 $7.6 $4.0 53% $0.7 9% $0.2 3% 1997 $6.7 $3.3 49% $0.7 10% $0.2 3% Source: U.S. Bureau of the Censu s. 2004c. “ Pump and Pumping Equipment Manufact uring : 2002.” 2002 Econom ic Census Manufacturing Industry Ser ies . EC02-311- 333911(RV). Washi ngt on, DC: U.S. Bureau of

the Censu s. Table 1. As shown in Table 2-4, the variable production costs include materials (including energ y) , and labor. Of the se cate gories , materia ls (includ ing fue l and ene

rgy ) repre sent 55 to 60 percent of the total product value. Within the materials categ ory, diesel and se midiesel engines account for approximately 1.8 percent of product value in 2002. L abor expenditures account for approximately 8 percent, and other costs such as capital, transporta tion, marketin g, and markup re

present the rema ining 35 p ercent
. Table 2-4.Air and Ga s Compr essor Ma nufacturing: 1997 to 20 02 ($billion)

Year Value of

Shipments Cost of

M ater ials Co st as a

Share of Product Va l ue ( %) Labor Co st as a Share of Product Va l ue ( %) Capital Co st as a Share of Product Va l ue ( %) 2002 $4.8 $2.7 56% $0.4 8% $0.2 4% 2001 $5.6 $3.0 54% $0.4 7% $0.2 4% 2000 $6.2 $3.3 53% $0.4 6% $0.2 3% 1999 $5.7 $3.0 53% $0.4 7% $0.2 4% 1998 $5.7 $3.1 54% $0.5 9% $0.2 4% 1997 $5.6 $3.1 55% $0.5 9% $0.2 4% Source: U.S. Bureau of the Censu s. 2004c. “ Pump and Pumping Equipment Manufact uring : 2002.” 2002 Econom ic Census Manufacturing Industry Ser ies . EC02-311- 333911(RV). Washi ngt on, DC: U.S. Bureau of

the Censu s. Table 1.2-5

2.1.1.3 Irrigation Systems The U.S. Economic Census classifies irrigation equipment under “F arm Machinery and Equipment Manufacturing” (NAI CS 333111). This U.S. industry comprises establishments primarily eng aged in manufacturing a gricultural and farm machinery and equipment, and other turf and grounds car e equipment, including planting, harvesting, a nd grass mowing equipment (except lawn and garden-ty pe). As shown in Table 2-5, the variable production costs include materials (including energ y) , and labor. Of the se cate gories , materia ls (includ ing fue l and ene

rgy ) repre sent 52 to 57 percent of the total product value. Within the materials categ ory, diesel and se midiesel engines accounted for appr oximately 2.2 percent of produc t value in 2002. Labor expenditur

es acco unt for a pproximate ly 9percen

t, and othe

r costs su ch as ca pital, transpor tation, ma rketing , and mar

kup repr esent the remainin g 39 per cent. Table 2-5.Farm

Machiner

y and Equipme nt Manufacturing: 199 7 to 2002 ( $billion)

Year Value of

Shipments Cost of

M ater ials Co st as a

Share of Product Va l ue ( %) Labor Co st as a Share of Product Va l ue ( %) Capital Co st as a Share of Product Va l ue ( %) 2002 $14 .7 $7.7 52% $1.3 9% $0.3 2% 2001 $14 .1 $7.6 54% $1.3 9% $0.3 2% 2000 $13 .5 $7.7 57% $1.4 10% $0.3 2% 1999 $11 .8 $6.4 54% $1.3 11% $0.3 3% 1998 $16 .5 $8.5 52% $1.5 9% $0.4 2% 1997 $16 .0 $8.4 53% $1.6 10% $0.5 3% Source: U.S. Bureau of the Censu s. 2004a. “ Farm Machinery an d Equipment Manufact uring : 2002.” 2002 Econom ic Census Manufacturing Industry Ser ies . EC02-311- 333111(RV). Washi ngt on, DC: U.S. Bureau of

the Censu s. Table 1.

2.1.2 Potential Cha

nges in Mate

rial Inputs Under the NSPS, subject nonemergency stationary CI

internal combustion engines must be certified to meet Tier 4 emission standards for NO x , NMHC, PM, and CO (Parise, 2005a, 2005b). To address emissions of these pollutants, several chang es in the manufacturing design and/or mate rial inputs have been considered. Among those ar e the follo wing:2-6 C catalyzed diesel pa

rticulate filters (CDPFs): Tier 4 emission standards for engines greater or equal 25 horsepower (hp) are based on the use of this control technology . It is estimated CDPFs will reduce PM emissions by more than 90 percen t, and als o reduce

NMHC and CO

emissions by a

signific ant amoun

t.

C NO x adsorbers: Tier 4 emission standards for engines g reater or equal to 75 hp are based on the use of this control technology . It is estimated NO x adsorbe rs will reduce NO x emissions by 90 percen t.

C lower-sulfur fuel: the owners and operators of the subject stationary CI internal combustion engines will be required to use diesel fuel containing 500 parts per million (ppm) sulfur or less by October 1, 2007. T his requirement will be lowered to 15 ppm (ultra-low sulfur diesel or ULSD) by October 1, 2010. 2.2 The Dem and Side The demand for diesel equipment is derived from consumer de mand for the services and products the equipment provides. We describe uses and consumers of these products as well as pr ovide exam

ples of su bstitution p ossibilitie s in consum ption. Resu

lts of econometric estimate s of elas ticities f

or downst ream se rvice a nd product markets

are inc luded as well. 2.2.1 Generato rs and Weldi ng Equipment Genera tor sets pr ovide power for prime, sta ndby , and pea

king power indust

rial, commercial, and communications facilities. Prime power units ty pically have lowe

r horsepower ratings while standby units have higher horsepower rating s. Potential substitutes including natural gas ge

neration units and a recent industry study sugg ests over 1/3 of 1,000 hp or higher sets use fuels other than diesel (Rhein Associa tes, 2002). However, diesel-engine gener

ators appear to be still preferred in remote/of fsite agriculture and construction uses (EPA, 2004). EPA’s profile of nonroad welding machines found that similar substitution issues exist for on-site versus remote locations (EPA, 2004). On-site facility welding can be accomplished with electric (AC or battery -operated) units, an arrang ement not possible for off-site uses. Gas welders provide a third option, one that is not dependent on a local infrastructure. In Table 2-6, we use

the latest detailed Benchmark I nput-Output Data report by the Bureau of Economic Analy sis (U.S. BEA, 2002) to identify industries that use generators and welding equipment. Note that these data include all ty pes of generators and welding2-7

Table 2-6. Generator Set and Welding Equipment Use by Industry: 1997

Com modity Code IO-C odeD etail_I-O

Descri ption Industry

Code IO-CodeDetail _I-O Description Use

Value Direct Requirem ents Coefficients a 335312 Motor and g enerator

manufacturing 333415 AC , refrigera tion, and force d air heating 1,36 4.2 6.23%
811300 Comm ercial machinery r epair and

mainten ance 453 .4 1.38%
333911 Pump and pumping equipment

manufacturing 451 .4 6.97%
335312 Mo tor and generator manufacturing 408 .7 3.46% 334119 Other computer periphera l equipment manufacturing 398 .7 1.67%
333992 W elding and soldering

equipment manufacturing 811300 Comm ercial machinery r epair and mainten ance 408 .3 1.24%
332312 Fabricat ed stru ctural metal manufacturing 170 .5 1.13%
811400 Household go ods repair and

mainten ance 140 .9 0.57%
333298 All other indust rial machinery manufacturing 107 .3 1.34%
230220 Commercial and i nsti tut ional

buildings 61 0.03%

Note: The datainc lude gener ator s and w eldi ng equi pment that

is not a ff

ecte d by the propos ed NSPS.

a Th ese va lues sho w the am ount o f the com mod ity require d to p rod uce $ 1.00 of the ind ustry’s outp ut. Source: U.S. Bureau of Eco nomic Analysis. 2002. 19 97 B enchmark Input-Output Accounts: Detailed Make Table, Use Table and Dire ct Requ ire ments Table. Tables 4 and 5. equipment and are not restricted to stationary diesel-powered equipment affected by the NSPS. For a

ll gene rators a nd weldin g and so ldering

equipmen

t, NAI CS 33415 (A C, refrigeration, and forc ed air heating) is the large st users of generators, and other industries that are relatively

large demanders include pumping e quipment manufacturing, genera tors and welders manufacturing, and ma chinery r epair. NAICS 811300 (Commercial mac hinery repair and maintenance) is the largest user of welding and soldering e quipment; other major users include fabricated metal manufacturing, household goods repair, and other industrial machi nery manuf

acturi ng.2-8

2.2.2 Stationary P

umps and Compressor Equipment The construction industry is an important consume r of pump and compressor equipmen t; as a re sult, dema

nd for thi s equipme

nt fluctua tes with c onstruct

ion activity. Oil field drilling and well servicing applications are primary consumer

s of high horsepower equipmen t such as d

rills and c ompressors. Dema nd in these

areas i s influenced by

chang es in fu el pr ic es an d c ha ng es in ov er al l e co no mi c a ct iv it y. In Table 2-7, we use

the latest detailed Benchmark I nput-Output Data report by the Bureau of Economic Analy sis (U.S. BEA, 2002) to identify industries that use pumps a nd compressor equipment. Again, these data include a ll types of pumps and compressor equipment and are not restricted to stationary diesel-powered equipment affected by the NSPS. Nonagricultural demanders of pumps and pumping equipment include r ailway trans porta tion , nonf

arm si ngle fam ily home s, and semic onduc tor ma chine ry manufac turin g. Major demanders of compressor equipment include construc tion of single-family

homes and additions , and manu

factur ing of c ompressor equipme nt, motor v ehicle p arts, an d plastic products

. 2.2.3 Irrigation Demand f or irrig ation equ

ipment is dr

iven by farm ope rators’ supply

decision s, optimal replacement considerations, and climate and wea ther conditions. The National Agriculture Statistics Service (NASS) 2003 Farm and Ranc h Irrigation Survey (USDA-NASS, 2004) shows the top five states ranked by total acres irrigated are Ca lifornia, Nebraska, Texas, Arkansas, and Idaho. The survey showed that approximately 500,000 pumps wer

e used on U.S. far ms in 2003 with ener

gy expenses t

otaling $1.5 billion dollars

. Electricity is the dominant form of e nergy expense for irrigation pumps, accounting for 60 percent of energy

expenses. Diesel fuel is second (18 percent), followed by natural gas (18 percent), and other forms of ener gy ( 4 percent). The report also notes that 411 pumps were powered by

solar or other renewable ener gy sour ces. In 2003, farme

rs and ranchers spent approxima

tely $13,000 pe

r farm on irriga tion investments.2-9

Table 2-7. Pumps and Compressor Equipment Use by Industry: 1997

Com modity Code IO-C odeD etail_I-O

Descri ption Industry

code IO-CodeDetail _I-O Description Use

Value Direct Requireme nt s Coefficients 333911 Pump and pumping

equipment manufacturing 482000 Rail transport ati on 508 .4 1.34% 230110 New residential 1-unit structures,

nonfarm 208 .1 0.12%
333295 Semiconductor machinery

manufacturing 173 .7 1.64%
230210 Manufacturing and indus tri al

buildings 92.6 0.34%
213111 Drilling oil and gas we lls 77.7 0.82% 333912 Air and gas compres sor

manufacturing 230110 New residential 1-unit structures, nonfarm 211 .9 0.12%
333912 Air and gas compres sor

manufacturing 115 .0 2.22%
230130 New residential additi ons and

alterati ons, nonfarm 56.1 0.10%
336300 Mo tor vehicle parts manufacturing 50.0 0.03% 32619A Plastic s plum bing fixtu res and all

other plastics p rod ucts 50.0 0.08%

Note: The datainc ludes pumps an d compres sor e quipment t hat is not a ff

ecte d by the NSPS.

a Th ese va lues sho w the am ount o f the com mod ity require d to p rod uce o ne do llar of the in dustry's ou tput. Source: U.S. Bureau of Eco nomic Analysis. 2002. 19 97 B enchmark Input-Output Accounts: Detailed Make Table, Use Table and Dire ct Requ ire ments Table. Tables 4 and 5.
2.2.4 Empirical Data Elasticities Stationary diesel equipment is used in the production of a variety of goods and service s. Econom

ic theor y su ggests the dema

nd for sta tionary diesel e quipment is

strongl y influenced by the

elasticity of final dema

nd for the product or service the equipment is used to produc e. EPA’s n

onroad d iesel ec onomic analy sis (EPA, 2 004) iden tified ke y ma rkets where stationary

diesel equipment was used and econometrically estimated demand elastici ties for t hese mar kets. De mand ela sticities measure the perc ent chan

ge in quantity demanded in response to a percent change in price. As shown in Table 2-8, final product/service demand is inelastic, that is, not very responsive to changes in prices. For example, a one percent2-10

Table 2-8. Empirical Demand Elasticity Estimates: Final Product Markets Where Stationary Diesel Equipment is Used
Stationary G en Sets and W elders Stati onary Pumps and Compressors Stati onary Irr igati on Syst ems Demand Chara cter ization Derived dema nd Derived dema nd Derived dema nd Final Product Market and De man d E lasticity ( 0 D ) Manufacturing –0.6 EPA (2004) Eco nom etric estim ate (inel asti c) Manufacturing –0.6 EPA (2004) Eco nom etric estim ate (inel asti c) Agriculture –0.2 EPA (2004) Eco nom etric estim ate (inel asti c)
increase in agricultural price s leads to only a 0.2 perc ent decline in demand for agricultura l output. 2.3 Industry Organization To estimate the economic impacts of a regulation, it is important to have an understa nding of industry organ ization. We d

iscuss ke y is sues in this

industry , identify firms and small businesses participating in the market, and discuss issues related to pricing behavior in these markets. 2.3.1 Diesel Engines: The Equipment Firm ’s “Make” or “Buy” Decision Vertica lly i ntegra ted firms own a com

bination o f “upstr

eam” an d “downst

ream” production operations; for example, vertically integrated diesel equipment manufacture s make the engines used in equipment rather than buy diesel engines from independent diesel engine manufacturers. Althoug h there are several reasons firms may choose this structure , two frequently c ited benefits are reducing transa ction costs associated with input purchases and taking advantage of te chnological economies that arise throug h integrated production structures (Viscusi, Vernon, and Harring ton, 1992). A review of the Power Systems Research (PSR) data for 2000 shows that vertical operations are more likely to occur in diesel-powered gener ator set a nd irrig ation sy

stem indust

ries rel ative to the other d irectly affected mark

ets. Approximat

ely 30 to 40 pe

rcent of manufactured die sel engines in the se equipment mark ets were c onsumed in

ternally by i ntegra ted manuf

acture rs in these

applica tions.2-11

2.3.2 Defining the Products that Constitute the Market To assess market structure, we need a c lear definition of the “market(s)” along geog raphic a nd product dimension s. There are two d istinctive product c haract eristics we use to define the products. First, consumers are more likely to view products with similar horsepower ratings as close substitutes, so it seems reasonable to delineate the markets by horsepower categories de fined in the engineering cost ana lysis and by

EPA (2004). Second, after r eviewing the EPA ind

ustry charac terization for the

Clean Air

Nonroad Diesel Ru

le (EPA, 2004), the California Air Resources Board’ s (CARB’s) Staff Report for the Airborne Toxic Control Measure for Stationary Compression-I gnition Engines (CARB, 2003), and a private industry study

of the diesel engine markets (Rhein Associates, 2002) , we have identified three broad nonemerge ncy stationary

diesel equipment applications where buy ers and selle rs would g eneral ly b e unwillin g to shif t consumpt

ion/produ ction among gr

oups. These include generator sets a nd welding equipment, pumps and compressors, and irriga tion systems. Market data associated with these applications are discussed in Section 2.4. 2.3.3 Key Firms Curre ntly Partic

ipating in the

se Market

s EPA identified key firms in ea ch of the markets using market share da ta from 2000 (PSR, 2004) . As discu ssed below, sales a re conc entrate d among

a few top firms ide ntified in each market (see Table 2-9)

. 2.3.3.1 Generat

ors and W

elders Sales lea ders in the diesel -power

ed generator

set marke

t include two Kore an firms (Korean GenSets and Daewoo Hea vy I ndustries & Machinery

Ltd.) and Honda Motor Company. L arge public firms L incoln Electric and Illinois Tool Works dominate sales of diesel-powered welding equipment. Hoovers identifies Lincoln Electric as a lea ding manufacturer of arc-we lding, cutting products, and welding supplies including a rc-welding power s ources , auto mated wire-f eedin g systems , and c onsum able el ectrod es for a rc-wel ding. Its major competitor is I llinois Tool Works, a diversified company that makes produc ts in the automotive, construction, paper products, and food and bevera ge industries (Hoovers, 2005). 2.3.3.2 Pumps and

Compresso rs Lea ders in the pump sec

tor includ e a small privately

owned bus iness, Pac er Pumps, and a public company , Gorman-Rupp Company. Gorma n-Rupp makes pumps used in a variety of industr

ies, inclu ding ag ricultur e and construction work, se wage t reatmen t,2-12

Table 2-9. Firm Market Shares by Equipment Market: 2000

M arket Share Pumps Pacer Pumps 13% Gorma n-rupp Comp any 11% Godwin Pumps of America 9% Air and Gas Comp ressors Ingersoll- rand 43% Atlas Copco ab 17% Sull iar Corp. 13% Hyd raulic Pow er Units Hydra- techPumps 26% Grif fi n Dewat eri ng Corpor ati on 14% Blount Inc

. 14% Genera tor Sets Ko rean G en-sets 13% Daewoo HeavyInd. 11% Honda Motor Company Ltd. 11% Welders Ill inois Tool W orks Inc

. 43% Linco ln Elec tric 55% Irrigation Sets Springfield Remanufacturi ng 28% Deere & Comp any 27% Tradewi nds Power

Corporat ion 19% Sour ce: Power

Sys tem s Reg is try ( PSR). 2004. OELin kTM. <http: // www. power sy s. com/ OELink .h tm>.
petroleum refining, agriculture , and fire fighting, as well for H VAC and military applications (Hoovers, 2005). The top three pump makers acc ounted for only one- quarter of pump sales in 2000. Industrial machinery giant Ing ersoll-Rand led sales in the air and gas compre ssor market in 2000. Atlas Copco AB and Sulliar Corporation (a division of Hamilton Sundstrand Corpora tion) are other ke y pl ayers in this

market. 2.3.3.3 Irrigation Equipment Sales leaders in this market include Springfield Remanufactur ing, Deere and Company, and Tra

dewinds Power Corp oration. Deere & Compan

y is one of the

two larg est makers of farm equipment (Hoovers, 2005). Tra dewinds Power is a small private firm that makes a r ange o f engines for the power gener ation indu stry

as well a s power u

nits, pump sets, and transmiss ions for in

dustrial a nd irrig ation use

(Hoovers, 2005). Together, thes e2-13

three companies account for approximately 75 percent of the diesel-powered irrigation system market in 2000. 2.3.3.4 Diesel Engines Engine production leaders for these mar kets include well-known domestic names Deere and Company , Caterpillar, and Cummins. Kubota Engine America, a subsidiary of Japanese Kubota Cor

poration , is anothe

r major se ller in thi s market, primaril y of small engines in industrial, agricultural, construction, and powe r generation equipment. Other Asian competitors are Korean companies Kukje Ma chinery Co. L

td and Daewoo Heavy Industries & Machiner

y L td. European competitors include German companies Deutz AG and Motorenfabrik Hatz, which owns North American subsidiary Hatz Diesel of America, Inc. Small businesses in this industry include Wisconsin Motors (owned by V&L Tools). Wisconsin Motors prod

uces die sel engines for

a small nic

he marke t and ser ved as a S mall Entity Representative (SER) during the Small Business Advocacy Review Panel process for the Clean Air Nonroad Diesel Rule (EPA, 2004, p 11-8). 2.3.4 Des cri

ption of Smal l and Lar ge F irms Small entities include small businesses, small organizations, and small governmental jurisdictions. For purposes of assessing the impacts of the CI NSPS, a small entity is defined as

C a small business whose parent company has fewer than 1,000 employ ees (for NAICS 335312 [Motor and Generator Manufa cturing]) or 500 employees (for NAICS 333911 [Pump and Pumping Equipment Manufacturing], NAICS 333912 [Air and Gas Compressor Manufacturing], and NAICS 333992 [Welding and Soldering Equipment Manufacturing]).

C a small governmental jurisdiction that is a government of a c ity, county, town, school district, or special district with a population of fewer than 50,000.

C a small or

ganizat ion that is a

ny n ot-for- profit en terprise , which is i

ndepend ently owned and operated and is not dominant in its field. To identify sales and e

mployment charac

teristics of affected parent companies, we use a company

database developed for small business analy sis of the Clear Air Nonroad Diesel Rule (EPA, 2004). Since the rule does not affect all companie s included in the database, the analysis only includes companies that produce the following type s of equipmen t:2-14 C Pumps and c ompressors (Pump a nd Pumping

Equipment

Manufac turing [N AICS 33391 1] or Air and GasCompr esso r Man

ufacturin g [NA ICS 33391 2])

C Welders and generators (Motor and Ge nerator Manufacturing [NAICS 335312] or Weldi ng a nd Sol deri

ng Eq uipme nt Man ufacturin g [NA ICS 33399 2]) We identified 60 small companies and 44 large companies with sales data . Using the data, we found 62 companies manufacture products that a re included in the Pump and Pumping Equipment Manufacturing (NAI CS 333911) or Air and Gas Compressor Manufacturing (NAICS 333912) industries and 30 companies manufa cture products included in the Motor and Generator Manufacturing (NAICS 335312) or Welding and Soldering Equipment Manufacturing (NAICS 333992) industries. The remaining 12 companies manufacture equipmen t in both PSR se gments. The ave rage small firm’ s annual s ales ar e approximately $30 million compared to nearly $6 billion for lar ge firms. The averag e small firm employed 100 people while the average lar ge firm employ

ed over 20,000 people. 2.3.5 Pricing Be

havior in Equi

pment Markets In the Clean Air

Nonroad Diesel Ru

le, EPA ar

gued that the competitive assumption

is “widely acc epte d economic

practice for t his ty pe of anal ys is (se e, for exam

ple, EPA [2 000], p. 126), especially

in cases where existing analysis suggests that mitigating factors limit the potential for raising price above marg inal cost” (EPA, 2004, p. 10-5). The mitigating factors cited in the nonroad rule include significant levels of domestic a nd international competition and significant excess capacity enabling competitors to quickly respond to changes in price. In addition, there were

no indications of barriers to entry or evidence of high levels of strategic behavior in the price and qua ntity decisions of the firms. Our re view of the availab le industr

y da ta sugg ests simila

r conditions are lik

ely to be pre sent in the

markets included in this ana

lys is. 2.4 Market Data To perform the economic impact analy sis, we compare baseline market conditions for affected mark

ets with c ounterf

actual c onditions produced under a new policy. This comparison requires developing a datase t for generator sets and welde rs, pumps and compressors, and irrigation equipment markets for the y ear 2015, the baseline y

ear of analysis. I n this section, we describe elements of the dataset and include inf ormation on quantities and prices for these three markets tog ether with historical and projected data.2-15

2.4.1 Baseline Quantities EPA has estimated that there will be approximately 16,000 ne w stationary nonemergency

CI engines produc ed in the United States in 2015. This is 20 percent of the total population of stationary CI engines (which is 82,000). The other stationary CI engines are used for emerge

ncy applica

tions (such as producing power when elec tric generation from the local utility is interrupted, pumping wa ter in case of fire or flood). The majority (10,200, or 62 percent) of stationary nonemergency

CI engines will be used in the generator sets and welders equipment market, with 32 percent of the eng ines used in the pumps and compressors market, and 6 percent used in the irrigation equipment market (see Table 2-10). Under the assumption that there is one-to-one corresponde nce between engines and equipment, it is reasonable to assume that these statistics reflect the equipment population as well. Table 2-10. Baseline Quantities for Engines and Equ ipment: 2015
Stationary Nonemergency Generator Sets and Welders Stationary Nonemergency Pumps and Com pressors Stationary Nonemergency Irr igat ion Syst ems Grand Total 51–75 hp 51–75 hp 50–100 hp 1,066 693 272 76–100 hp 76–100 hp 101-600 hp 1,532 1,343 707 101–175 hp 101–175 hp 2,479 1,111 >176 hp >176 hp 5,132 2,011 Total Total Total 10,210 5,158 979 16,347 Sou rce: So rr el s, La rr

y, EPA , e- ma i l t o Rut h Me ad , ERG and Kat he ri ne Hel l er

an d Br ook s D ep ro , RTI Interna tional. C I engine pop ulation b y NA ICS . Apr il 20, 2 005

.

2.4.2 Baseline Prices For the Clean Air Nonroad Diesel Rule, EPA collected pr ice data for the nonroad diesel equipment from a variety of sources, including the U.S. General Service s Administration and various Web sites. A relationship between price and horsepower was obtained using a linear regr ession technique (see Guerra [2005], p. 2). Using these linear equations and median horsepower for eac h market, we estimated national prices for stationary nonemer

gency CI equipment. As shown in Table 2-11, estimates range from2-16

$7,200 to $86,200 and vary a mong applications. In ea

ch horsepower categor

y, prices for small horsepower pumps and compressors and irriga tion systems are ty

pically highe

r than those for gener ator sets and weld ers. 2.4.3 Historical

Data Despite significant declines in 2000 sales, gene rator sets and welding markets gr ew at an aver age a nnual ra te of 9 pe rcent be tween 19 90 and 200 0. As show n in Table

2-12, this growth was led by

low horsepower equipment (less than 100 hp). I rrigation equipment showed similar strong growth rates during the period (7 percent), followed by pumps and compressors. Table 2-11. Baseline Equipment Prices: 2015
Stationary Nonemergency Generator Sets and Welders Stati onary Nonemergenc y Pumps and Compressors Stationary Nonemergency Irr igat ion Syst ems 51–75 hp 51–75 hp 50–100 hp $7,231 $13,960 $42,247 76–100 hp 76–100 hp 101–600 hp $10,101 $19,499 $75,815 101–175 hp 101–175 hp $15,840 $30,578 >176 hp >176 hp $44,535 $86,192 Note: Calcul ated computed us ing Guerra (2005) and midpoint

value of horsepower r ange.2-17

Table 2-12. Historical Unit Sales Data by Market: 1990–2000

2000 1999 1998 1997 1996 1995 1994 1993 1992 1991 1990 Stationary Nonemergency Generator Sets and Welders 50–75 762 1,637 658 466 279 361 303 282 336 354 304 76–100 993 1,355 525 434 315 342 289 238 206 249 164 101–175 1,773 2,179 1,247 1,081 886 1,122 1,137 1,133 928 934 774 >176 2,926 2,591 2,588 2,093 1,485 1,792 1,708 1,607 1,522 1,577 1,415 Total 6,454 7,762 5,019 4,074 2,965 3,617 3,437 3,260 2,991 3,114 2,656 Stationary Nonemergency Pumps and C ompressors 50–75 289 357 354 334 285 285 254 203 204 254 297 76–100 494 460 421 384 326 321 289 260 228 258 266 101–175 427 459 508 533 590 604 517 456 436 531 504 >176 993 939 875 784 823 809 658 566 487 606 712 Total 2,203 2,216 2,158 2,036 2,024 2,017 1,718 1,485 1,355 1,649 1,780 Stat iona ry Nonem erge ncy I rri gati on Sys tems 50–100 81 72 62 65 75 72 60 57 65 70 63 101–600 663 620 584 528 511 477 460 410 360 328 293 Total 744 692 646 592 585 549 521 468 425 399 356
Sou rce: So rr el s, La rr

y, EPA , e- ma i l t o Rut h Me ad , ERG and Kat he ri ne Hel l er

an d Br ook s D ep ro , RTI Interna tional. C I engine pop ulation b y NA ICS . Apr il 20, 2 005

.

2.4.4 Projecti

ons Using 10-year growth data for eng ines (Sorrels, 2005), the Agency estimated that stationary nonemer

gency CI engine marke

ts will continue to grow at historical rates (see Table 2-13). The total affected population is estimated to g row from 11,700 to 16,300 engines between 2005 and 2015.2-18

Table 2-13. Projected Annual Unit Sales for Nonemergency CI Engines: Selecte d Years
Hp Range 2005 2010 2015 50–75 1,314 1,551 1,788 75–100 2,068 2,593 3,119 100–175 3,148 3,701 4,255 175–300 3,029 3,710 4,391 300–600 1,203 1,368 1,532 600–750 172 189 205 750–1,200 391 453 515 1,200–3, 000 382 446 510 Over 3, 000 32 32 32 Total 11,738 14,042 16,347
Sou rce: So rr el s, La rr

y, EPA , e- ma i l t o Rut h Me ad , ERG and Kat he ri ne Hel l er

an d Br ook s D ep ro , RTI Interna tional. C I NS PS Co st Imp acts 05 -26-0 5. Jun e 1, 2 005

.3-1 SECTION 3 REGULATO RY PROG RAM COST ESTIMATES
Under the NSPS, subject nonemergency stationary CI

internal combustion engines must be certified to meet Tier 4 emission standards for NO x , NMHC, PM, and CO (Parise, 2005a, 2005b). To address emissions of these pollutants, several chang es in the manufacturing design and/or mate rial inputs have been considered. Among those ar e the follo wing:

C CDPFs: Tier 4 emission standards for engines gre ater than or equal to 25 hp are based on the use of

this contr

ol techno logy . It is

estimate d CDPFs wil l reduce PM emissions by more than 90 perc ent and also reduce NMHC and CO emissions by a signif icant amo unt.

C NO x adsorbers: Tier 4 emission standards for engines g reater or equal to 75 hp are based on the use of this control technology . It is estimated NO x adsorbe rs will reduce NO x emissions by 90 percent. The total estimate d costs of

the NSPS for

stationar y CI engine s are pr

esented in Table 3-1. The capital cost of control of the NSPS is estimated to be $67 million in 2015, the model year f or which stationary CI

internal combustion engines would have to meet final Tier 4 emission standards. The annualized cost of control of the NSPS is estimated to be $36 million in 20 15. The total annua lized cost i

ncluding accumul ated re

porting costs in 20 15 is estimated to be $57.1 million. Tabl e 3-1. Summary of To tal Co sts As socia ted w ith th e NSPS

Type o f Cost Total Cost s ($mi ll ion) 2011 2012 2013 2014 2015 2016 2017 Cap ital control cost 31.0 40.1 42.4 62.7 66.8 68.6 70.3 Annual co ntrol cost 4.4 10.4 16.8 26.1 36.1 46.3 56.8 Reporting 10.2 12.5 15.0 17.5 20.0 23.0 25.6 To tal annualized co st 15.4 23.8 32.7 44.5 57.1 70.1 83.41 M as-Colell et al. (1995 ) and V ives (198 7) pro vide a technical d iscussion of these issues. 4-1 SECTION 4 ECONOMIC IMPAC T ANALYSIS: METHODS AND RESULTS

The EIA uses a combination of the ory and simulation modeling to eva luate potential behavior changes associated w ith a new regulatory

program. The goal is to estimate the impact of the reg ulatory progra m on produc ers and c onsumers . For this analy sis, we ch ose to use a par tial equil ibrium (PE ) modeling approac h for the affected mark

ets for two reason s. First, although these commodities may be an intermediate good used in the market, pric e changes that will occur in these markets will be small enoug h that production and consumption choices in related markets will be approximately unaffected. Similarly , changes in household income that could affect the demand for other pr oducts and services are expected to be small enough that they are not explicitly modeled. 1 In addition, an intermediate run approach is used in this EI A because it is likely that produc ers have flexibility to adjust se lected f

actors o f production but some of the fa ctors (e .g., ca

pital) stil l remain fixed. This lack of resource mobility captures potential transitory impacts on producers during the analy sis period. 4.1 Analytical Approach The CI NSPS and Cle an Air Nonroad Di esel rule affect similar ma

rkets, so their impacts can be modeled in similar ways. Both rules increase the costs of manufacturing engines, the demand for which derive s from the demand for the equipment in which the engines will be used. However, size and other diffe rences between the rules led to the development of a separate model for the CI NSPS analysis. For example, the Nonroad D iesel Economic Impact Model (NDEI M) models derive the demand for equipment and engines from those “final” application markets; in the CI NSPS EPA simplified this approach and only models die sel equipment mark ets that us e affe cted eng

ines in the ir production proc ess. EPA characterizes the response of equipment demand to c hanges in equipment prices with an2 By d oing this, w e imp licitly assum e engin e ma nufactu rers fully pa ss on the contro l costs up the sup ply cha in to the equipment market . The elas tici ty par ameters and resul ts of the numer ical simul ations in t he nonroad economic impact analysis support t he use of thi s simplif

ication. 4-2 elasticity para

meter. 2 We discuss and compare NDEI M’s characterization of equipment demand responses and the CI N SPS method below. NDEIM’s relationships explicitly link the demand for engines and equipment to the market b ehavior

in an “ap plication ” market. A deman

d curve s pecifie d in terms o f its downstream consumption is referred to as a derived de mand curve (see Figure

4-1 for a graphical illustration of how a derived demand c urve is identified). Consider an event in the eq ui pm en t m ar ke t t ha t c au se s t he pr ic e o f e qu ip me nt to in cr ea se by ) P (such as an incre ase in the price of engines). This increase in the price of equipment will cause the supply curve in th e a pp li ca ti on ma rk et to sh if t, le ad in g t o a de cr ea se d q ua nt it y of ac ti vi ty ( ) Q C ). The chang e in activ

ity l eads to a

decrease in the q

uantity demande d for equ

ipment ( ) Q E ). The new point (Q E – ) Q E , P + ) P) traces out the derived demand curve. The supply and demand function in the application market are needed to identify the derived demand in the construction equipment market. An alternative approach to identify ing the demand response is to derive an expression for the derived demand elasticity parameter using economic theory . Economist Alfred Marshall identified four factors that influence an industry ’s price elasticity of demand for a factor (Hicks, 1963; Lay

ard and Walters, 1978). We restate these “rules” in ter ms of the industry elastici ty o f demand

for equi pment ( > IE ) and the two factor production functions (equipment and labor). The (absolute) elasticity of demand for equipment in industry varies directly with

C the (absolute) elasticity of demand for the product or service the industry produce s ( 0 D ),

C the cost share of equipment (vE ) in production,

C the elasticity of supply of other factors (i.e., labor) ( 0 S ), and

C the elas ticity

of substitu

tion betwe en equipment and ot her factors ( F EL ).4-3

Figure 4-1. Derived Dem and for Equipment from the Construction Industry3 The fixed propo rtions t echnology assumption is reas onable for this industry. For i nterested readers, t he expr ess ionfor

th e mor e comp li cat ed f ormu la th at in clu des th e el ast icityof s ubs ti tu ti on i s i ncl uded in J. R. Hic ks’ Theory of Wages , pp. 243-244. A si mil ar ex press ion i s re porte d in L ayar d and Walte rs ( 1978, p. 26 7). 4-4 It ca n be shown

that if the

producti on functi on exhibits c onstant r eturns to scale wi th fixed factor proportions technology (i.e., F EL = 0) 3 , price elasticity of

demand for equipment can be expressed a s a funct ion of the selecte d variab les includ ed in Marshall’s r

ules (Hic ks, 1963; Lay ard and Walters, 1978):

. (4.1)

This expre ssion can

be further simplif

ied by assuming

the supply

of other factors is perfectly elastic ( 0 S = 4 ). This simp lification is appro priate b ecause

we expect the cha

nges in producti on in the e quipment ma rket to be small enou

gh that t hey do not infl

uence prices in other fa ctor mar kets: > IE = v E × 0 D

. (4.2) Therefore, we can estimate this beha vioral parameter with cost share data and f inal product/service demand elasticities. 4.2 Diese l Equ ipmen t Mark ets Af fecte d by t he CI N SPS EPA identified three national competitive markets for the economic impact analysis—an appr

oach consistent with the geographic de finition and pricing behavior assumptions for nonroad diesel equipment markets within NDEI M. In selecting the competitive model, EPA argued that the competitive assumption is “widely accepted economic practic e for this

typ e of ana

lys is (see, for examp le, EPA [200 0], p. 126), especia lly in cases where existing analysis suggests that mitigating factors limit the potential for ra ising price above marginal cost” (EPA, 2004, p. 10- 5). The mitigating factors cited in the nonroa d rule include significant levels of domestic and international compe tition and significant excess capacity

enabling competitors to quickly respond to changes in price. I n addition, there were no indications of barriers to entry or evidence of high levels of strateg ic behavior in the pric e and qua

ntity decision s of the f

irms. Our prelimina

ry review o f the ava

ilable4-5

industry data and literatu re suggests simila

r conditions are lik

ely to be pre sent in the

markets included in this analysis. We used two

distinctiv e product chara cteristi cs to def

ine the pr oducts tha

t constitut

e the markets. First, consumers are more likely to view products with similar horsepower ratings as close substitutes, so it seems reasonable to delineate the mar kets by horsepowe

r categories defined in the engineering cost analysis and by EPA (2004). Second, a fter reviewing the EPA industry characterization for the Clean Air Nonroad Die sel Rule (EPA, 2004), the

Californ ia Air Res ources B oard’s ( CARB’s)Staff Rep ort for the Airbor ne Toxic Control Me asure f

or Stationary

Compressi on-I gnition E

ngines ( CARB, 200 3), and a private industry study

of the diesel engine markets (Rhein Associates, 2002) , EPA identified three broad nonemerg ency

stationar y di esel mar

kets whe re buy ers and s ellers in

the mark et would be unwilling to shift consumption/production among these products on a larg e scale. To characterize behavioral responses in these three markets, EPA uses existing model elastici ty p aramete

rs availa ble from N

DEIM model. The

U.S. Bur eau of E conomic A

naly sis (2002) a nd U.S. De

partmen t of Agr iculture (2004) pr

ovide dat a that all ow us to ap

proximate associated cost shares in final application markets. Table 4-1 pr ovides the baseline data set for the economic impact analysis, and Section 4.3 describes the partial equilibrium model equation s used for

the econ omic impac

t analy sis. 4.3 Overview of Partial Equilibrium Model We illustrate our approach for estimating market-leve l impacts using a numerical simulation model. Our method involves specifying a set of nonlinear supply and demand relationships for the three diesel equipment markets identified in Table 4-1, simplify ing the equations by transfor

ming them into a set of linear equations, and then solving the equilibrium system of equations (see, f or example, Fullerton and Metcalfe [2002]). 4.3.1 Market Supply First, we consider the forma

l definiti on of the e lasticity of supply with resp ect to changes in own price:

. (4.3)4-6

Table 4-1. Markets Included in Economic Impact Model
Stationary G en Sets and W elders Stati onary Pumps and Compressors Stati onary Irr igati on Syst ems Power Syste ms Research (PSR) appl ication descriptions Generator sets, welders Air compress ors, ga s compressors, hydro power units, pressure washe rs, pumps Irr ig ati on s ys tem s

Geographi c scope National National National Product groupings 4 horsepower categories 5 horsepower categories 2 horsepower categories Firm be havi or Perfe ct c ompeti ti on Perfe ct c ompeti ti on Perfe ct c ompeti ti on Baseline eng ine population See Table 4-2a See Table 4-2b See Table 4-2c Baseline year 2015 2015 2015 Sup ply elastic ity 2.9 EPA (2004) 2.8 EPA (2004) 2.1 EPA (2004) Demand char acte riz ati on Derived dema nd Derived dema nd Derived dema nd Final product market and dem and elasticity ( 0 D ) Manufacturing –0.6 EPA (2004) Eco nom etric estim ate (inel asti c) Manufacturing –0.6 EPA (2004) Eco nom etric estim ate (inel asti c) Agriculture –0.2 EPA (2004) Eco nom etric estim ate (inel asti c) Cost share (v E ) 7 % 1 % 2 % Estimated derived demand elasticity ( > IE ) –0.042 –0.006 –0.0044-7

Table 4-2a . Baseline D ata: None merge ncy Statio nary Dies el Gene rator Sets and Welders, 2015

Applic ation 51–75 hp 76–100 hp 101–175 hp >176 hp Quan tity (units) 1,066 1,532 2,479 5,132 Pric e ($/un it) $7,231 $10,101 $15,840 $44,535 Source s: U.S . Env ironm ental P rotec tion A genc y. 200 4. Final Regulatory Anal ysi s: Cont rol of Emis sions fr om Nonroad Diesel Engines . EPA 420-R- 04-007. Avail able from <ht tp: // www. epa. gov/

nonr oad- die sel/200 4fr .h tm>. So rr el s, La rr

y, EPA , e- ma i l t o Rut h Me ad , ERG and Kat he ri ne Hel l er

an d Br ook s D ep ro , RTI Int ernational . CI e ngi ne popul ati on by NAICS. April20, 2005.

Table 4-2b.Baseline D ata: None merge ncy Statio nary Dies el Pumps and Com pressors , 2015
Applic ation 51–75 hp 76–100 hp 101–175 hp >176 hp Quan tity (units) 693 1,343 1,111 2,011 Pric e ($/un it) $13,960 $19,499 $30,578 $86,192 Source s: U.S . Env ironm ental P rotec tion A genc y. 200 4. Final Regulatory Anal ysi s: Cont rol of Emis sions fr om Nonroad Diesel Engines . EPA 420-R- 04-007. Avail able from <ht tp: // www. epa. gov/

nonr oad- die sel/200 4fr .h tm>. So rr el s, La rr

y, EPA , e- ma i l t o Rut h Me ad , ERG and Kat he ri ne Hel l er

an d Br ook s D ep ro , RTI Int ernational . CI e ngi ne popul ati on by NAICS. April20, 2005.
Table 4-2c. Baseline Data: Nonemergency Stationary Diesel Irr igation Systems, 2015

M arke t Va riab le 50–100 hp 101–600 hp Quan tity (units) 272 707 Pric e ($/un it) $42,247 $75,815 Source s: U.S . Env ironm ental P rotec tion A genc y. 200 4. Final Regulatory Anal ysi s: Cont rol of Emis sions fr om Nonroad Diesel Engines . EPA 420-R- 04-007. Avail able from <ht tp: // www. epa. gov/

nonr oad- die sel/200 4fr .h tm>. So rr el s, La rr

y, EPA , e- ma i l t o Rut h Me ad , ERG and Kat he ri ne Hel l er

an d Br ook s D ep ro , RTI Int ernational . CI e ngi ne popul ati on by NAICS. April20, 2005.4-8

Next , we can use “h at” no tati on to trans form E q. (4. 3) to p ropor tion al chan ges and rearran ge terms: (4.4) where

= pe rc en ta ge ch an ge in th e q ua nt it y of ma rk et su pp ly, 2 s = market elasticity of supply , and

= percentage change in market price. By using this approach, we have taken the elasticity definition and turned it into a linear behavioral

equation that cha racter izes marke

t supply in the thre e diesel

equipmen

t markets

. To introduce the cost of controls associated with the reg ulatory prog

ram, we assume the perunit cost (c) leads to a proportional shift in the marginal cost of production. Under the assumption of perfect competition (price equals marginal cost), we can approximate this shift at the init ial equili brium poin

t as follow s:

. (4.5)

4.3.2 Market De

mand We can specify

a demand equation for each diesel eng ine equipment market as follows: (4.6) where

= percentage change in the quantity of marke t demand, > IE = market elasticity of de mand, and

= percentage change in market price.4-9

4.3.3 Equilibrium Solu tion Lastly , we specify

the market equilibrium conditions in three diesel equipment markets. In response to the e xogenous increase in production costs, the new equilibrium satisfies the condition that the change in supply equals the change in demand:

. (4.7)
We now have three

linear e quations i

n three u nknowns ( , , and ), and we can solve for the proportional pric e chang e in terms of the el asticity

parameters ( g s and 0 d ) and the proporti onal cha

nge in ma rginal cost:

. (4.8)

Given this solution, we can solve for the proportional change in mar ket quantity using the demand equation. 4.4 Results The model projects the NSPS will increase prices from 2 to 9.4 percent (se e Table 4- 3). Gene rator se t and weld

ing equ ipment mar

kets exper

ience the highe st relati

ve chan ge in baseline price (9.4 percent). Domestic produc tion declines by small amounts (less than 0.5 percent) because the (absolute) elasticity of demand f

or the final product or service that uses affected equipment and the cost share of equipment in production of these goods and service s is small. The national compl iance c ost estima tes are often use d to appro ximate the s ocial co st of the rule. The engineering analysis estimated annua lized costs of $57.1 million. In cases where the engineering

costs of compliance are used to estimate social cost, the burden of the regulation is typic ally measure d as falling solely on the affected producers, who experienc e a profit loss exactly equal to these cost estimates. Thus, the e ntire loss is a change in producer surplus with no change (by assumption) in consumer surplus, because no change in mar ket price is estimated. This is typically referred to as a “full-cost absor ption” scenario in which all factors of production are assumed to be fixed and firms are unable to adjust their output levels when face d with add itional co sts.4-10

Table 4-3. Summary of Economic Impacts: 2015

M arket-Level Impacts % C hange in Price % Chan ge in Quan tity Generator Sets and W elders 9.4% –0.40% Pumps and Comp ressors 3.7% –0.02% Irr ig ati on Sy st ems 2.0% –0.01% W elfare Impacts Change in Consumer Surplus –$37.629 Change in Producer Surp lus –$0.436 Recordkeeping cos ts not addressed i n market model –$19.009 Change in Total Surplus –$57.074
In contrast, EPA’s economic a nalysis builds on the enginee ring cost analysis and incorporates economic theory related to producer and consumer beha vior to estimate changes in market conditions. Owners of affected plants are economic agents that can make adjustmen

ts, such a s changing prod uction ra tes or alt ering i nput mixes, that will g eneral ly affect the market environment in which they operate. As producers chang e their production levels in response to a regulation, consumers are typically faced with changes in prices tha t cause them to alter the quantity that they are

willing to purchase. These chang es in price and output from the market-level impacts are used to estimate the total surplus losses/ga ins for two types of stakeholder

s: chemical consumers and owners of chemical plants. The nume rical sim ulation su ggests the cha

nges in p rice an d quantity are re latively small; thus, the economic approach and engineer ing cost approach y

ield approximately the same estimate of the total change in surplus ($57.1 million). However, the advantage of the economic approach is that it illustrates how the costs flow through the ec onomic system and identifies transitory impacts on stakeholde rs. As shown in Table 4-3, equipment consumers unambiguously see

reductions in surplus as the result of higher prices and r educed consumpti on ($37.6million). T he monitor

ing, re cordkee

ping, re cording , and rep orting c osts in 2015 total $20.2 million. They include $2.06 million for prime engines ( included in the model) and $19.009 million for initial notification ($5,768), certification ($845,000), and recording hours of nonemerg ency ope ration of emergency

engines ($18.158 million). The $19.0 million in costs are largely borne by eme

rgency engine operators and thus are not included in the model; instead, they ar e entered as a line item in the calculation of social costs of the rule (see Table 4-3).4-11 Table 4-4 presents detailed economic impact estimates for generator and welder equipment. Prices are projected to increase from 6.3 percent to 10.3 percent across the horsepower ranges, and qua ntities sold are projected to decline by less than 0.5 percent. For this segment of the industry, pur chasers of the genera

tor and welder equipment are projected to incur three-quarters of the social costs ($28.9 million), while produce rs of the generator and welder equipment are projected to lose less than $0.5 million. Table 4-4. Detailed Results for Generator and Welder Equipment: 2015

M arket-Level Impacts HP Range % C hange in Price % Chan ge in Quan tity 51-75 hp 6.3% –0.27% 76-100 hp 9.4% –0.40% 101-175 hp 8.9% –0.37% >176 hp 10.3% –0.43% W elfare Impacts Change in Consumer Surplus –$28.949 Change in Producer Surp lus –$0.417 Change in Total Surplus –$29.366
Table 4-5 presents detailed economic impact estimates for pump a nd compressor equipment. Prices are projected to increase from 2.4 percent to 4.9 percent across the horsepower ranges, and qua ntities sold are projected to decline by less than 0.03 percent. For this segment of the industry, pur chasers of the pump and compressor equipment are project ed to incu r the majority

of the soc ial costs ( $7.4 million ), while producer

s of the pu

mp and compressor equipment are projected to lose $0.02 million. Table 4- 6 presen

ts detaile d economi c impact e stimates f or irrig ation equ

ipment. Prices a re proje cted to increase

from 2.0 p ercent

to 2.2 per

cent across the horsepow er ranges, and quantities sold are projected to decline by 0.01 percent. Like the other two segments of the industry, purchase

rs of the irrigation equipment are projecte d to incur the majority of the4-12

Table 4-5. Detailed Results for Pump and Compressor Equipment: 2015

M arket-Level Impacts HP Range % C hange in Price % Chan ge in Quan tity 51-75 hp 3.3% –0.02% 76-100 hp 4.9% –0.03% 101-175 hp 4.7% –0.03% >176 hp 2.4% –0.01% W elfare Impacts Change in Consumer Surplus –$7.376 Change in Producer Surp lus –$0.016 Change in Total Surplus –$7.391
Table 4-6. Detailed Results for Irrigation Equipment: 2015

M arket-Level Impacts HP Range % C hange in Price % Chan ge in Quan tity 51-100 hp 2.2% –0.01% 101-600 hp 2.0% –0.01% W elfare Impacts Change in Consumer Surplus $ (1. 305) Change in Producer Surp lus $ (0. 002) Change in Total Surplus $ (1. 307)

social c osts ($1.3 million), w hile prod ucers of the irrigation equ

ipment ar e projected to lose less than $0.01 million. The NSPS wil l reduce

emissions o f SO 2 by re qui rin g th e us e of lo wer -su lfu r fu el. In the base line y

ear of a naly sis (2015) , new sta

tionary CI eq uipment su

bject to the NSPS will be r equ ire d t o us e ULS D fu el ( i. e., mu st con sum e fu el m eet in g a 1 5 pp m s ul fur st and ard ). In the abse nce of r egulat ion, these engine s might sti ll have be en using high-su lfur fue l. As a result of regulation, demand for UL SD fuel will increase and demand for high- sulfur No. 2 distillate will decline relative to an unregulated baseline. Estimated de mand for low-sulfur4-13

diesel fuel in 2015 is 63.2 billion gallons. This number includes demand for diesel fuels used in highway , nonroa

d, locomot

ive, and marine c ategor

ies (see Table 4- 7). Estima

ted prime engine fuel demand in 201 5 is 1.73 bil lion gal lons; ther efore, the expected incr

ease in demand f or low-su lfur dies el fuel e quals 2.74 percen t. On the o ther han

d, decre ase in demand for high-sulfur diesel fuel will be a substantial shar e of the remaining production of high-su lfur dies el fuel. Estimated 2015 hig h-sulfur fuel pro duction is

7.538 billi on gallons, and the ratio of prime engine fuel dema nd to high-sulfur fuel supply equals 22.94 percent (see Table 4-7). Table 4-7.Shift of Diesel Fuel Supply and Dema nd Quantitie s in 2015 (bil lion gallons)

Fuel Use Category Esti mated Demand f or Low -Sulfur Dies el Fuel a Esti mated Supply for HighSulfur Diese l Fuel a Highway 47.58

— Nonroad 10.34

— Locomo tive 3.13

— Marine 2.16

— He ating o il

— 7.54 Total 63.21 7.54 Estimat ed stat ionary

prime- engine fuel demand b 1.73 1.73

Ratio 2.74% 22.94%

a From Table 7.1.4-13, p7-61, Fin al Reg Supp Doc. b E-mai l f rom Larry Sor

rel s t o ERG and RTI, “An aly sis of av ail abil ity of ult ra l ow sul fur

dies el. ” June 7, 2005.5-1 SECTION 5 SMALL BUSINESS IMPAC T ANALYSIS
This regulatory

action will potentially affe ct the economic welfare of owners of facilities that will own or operate new CI internal combustion engines. The ownership of these facilities ultimately is in the hands of priva te individuals who may be owner s/operators that directly conduct the business of the firm (i.e., single proprietorships or partnerships) or, more commonly, investors or stockholders that employ others to conduct the business of the firm on their behalf (i.e., privately held or publicly trade d corporations). The individuals or agents that manage these f acilities have the capacity to conduct business transactions and make business decisions that affect the facility . The legal and financial responsibility for compliance with a regulatory action ultimately rests with these agents; however, the owners must bear the fina ncial consequences of the decisi ons. Envir onmental regula tions like t

his rule potentially aff

ect all businesses, large and small, but small businesses may have special problems in complying with such r egulations, because they

may have le ss specialized environmental expe

rtise or l imited ac cess to c apital for investin g in comp liance e quipment. The Regulatory

Flexibility Act (RFA) of 1980 re quires that special consideration be given to small entities affected by federal regulation. The RFA was amended in 1996 by the Small Business Regulatory Enforcement Fairness Act (SBREFA) to strengthen the RFA’s analytical a

nd procedural requirements. Prior to enactment of SBREFA, EPA exceeded the requirements of the RFA by requiring the preparation of a r egulatory flexibility analysis for every r ule that would have any

impact, no matter how minor, on any number, no matter how few, of small entities. Under SBREFA, however, the A gency decided to implem ent the RFA as writte n and that a regu latory flexibility

analy sis will be r

equired only for rule s that will have a significant

impact on a substantial

number of small entities (SISNOSE). I n practical terms, the a mount of an alysis of small en tities’ i mpacts has n ot changed, for S BREFA require d EPA to increase i nvolveme nt of small

entity stakehol ders in the rulema king pro cess. Thus, the Agency

has made additional efforts to consider small entity impacts as part of the rulemak ing proc ess.5-2

5.1 Description of Small Entities Affected Small entities include small businesses, small organizations, and small governmental jurisdictions. For purposes of assessing the impacts of the CI NSPS, a small entity is defined as

C a small business whose parent company has fewer than 1,000 employ ees (for NAICS 335312 [Motor and Generator Manufa cturing]) or 500 employees (for NAICS 333911 [Pump and Pumping Equipment Manufacturing], NAICS 333912 [Air and Gas Compressor Manufacturing], and NAICS 333992 [Welding and Soldering Equipment

Manufac turing]) ;

C a small governmental jurisdiction that is a government of a c ity, county, town, school district, or special district with a population of fewer than 50,000; and

C a small or

ganizat ion that is a

ny n ot-for- profit en terprise , which is i

ndepend ently owned and operated and is not dominant in its field. 5.2 Small Busine ss Scree ning Analysis In the next step of the analy sis, we assessed how the regulatory program may influenc e the pro fitability of ultimat

e paren t compani es by

compari ng pollut ion contr ol costs to total sales. To do this, we divided an ultimate parent company ’s total annual compliance cost by its reported re

venue (see the following equation): (5.1)

where CSR = cost-to-sales ratio, TACC = total ann

ualized c omplianc e costs, i = indexes the

number of

affected plant s owned by company j,

n = number of affected plants, and TRj = total revenue fr om all ope

rations o f ultimate

parent c ompany

j. This metho d assumes

the aff ected a company cannot sh

ift pollution control

costs to consumers (in the form of higher market pr ices). Instead, the c ompany experience s a one-forone redu ction in pr

ofits.5-3 To identify sales and e

mployment charac

teristics of affected parent companies, we used a company

database developed for small business analy sis of the Clean Air Nonroad Diesel Rule (EPA, 2004). Since the rule does not affect all companie s included in the database, the analysis only includes companies that produce d the following type s of equipmen t segme nts:

C pumps and c ompressors (Pump a nd Pumping

Equipment

Manufac turing [N AICS 333911] or Air and Gas Compressor Manufacturing [NAICS 333912]) and

C welders and generators ( Motor and Generator Manufacturing [NAICS 335312] or Welding and Soldering Equipment Manufacturing [NAICS 333992]). The statistics included in the database come from PSR and other publicly available resources such a s the f

ollo wing:

C Business & Company Resource Center . A singl e database for c ompany profiles , company brand inf ormation, ranking s, investme nt repor ts, compa ny h istories, chronologies, full-text articles, investment reports, industry overviews, and financial and trade association data. http://www.ga le.com/servlet/Item DetailServlet?region=9&imprint=000&titleCode=GAL 49&type= 1&id=115085.

C Hoover’

s Online . This electronic database is an excellent source of infor mation on U.S. public and private companies. Users can searc h for companies by

name, ticker symbol, or key

word. It provides corpora te ownership, sales, net income, and employment. L

inks are also provided to the company ’s Web site and those of top competitors (if available), SEC filings in EDGAR Online, investor researc h reports , and new s and commentary . http://ww w.hoover s.com/.

C Referen ceUSA . The ReferenceUSA database c ontains, in module format, detailed information on more than 12 million U.S. businesses. Information is compiled from the f ollowing public sou

rces: mor e than 5,6 00 Yellow

Page a nd Busine ss White Page telephone directories; annual reports, 10-K s, and other Securities and Exchange Commission (SEC) information; Continuing Medical Education (CME) directories; federal, state, provincial, and municipal government data; Chamber of Commerce

informat ion; lead ing business mag azines, tr ade publi cations, newsletters, major newspapers, industry and specialty dire ctories; and postal service information, including both U.S. and Canadian National Chang e of Address updates. One disadvantage of this database is that it only reports sales and employme

nt rang es. For

this analy

sis, we de veloped a point estimate for

these values ( typ ically the media

n). http:// www.re ferenc eusa.com/.5-4 C Dun & Bradstre

et’s Million Dollar Dir

ectory . The D&B Million Dollar Directory provides informat ion on ove

r 1,260,0 00 U.S. lea

ding pub lic and pr

ivate businesses. Company information includes industry information with up to 24 individua l eight- digit Sta

ndard I ndustria l Classific ation (SIC) codes , size crit eria (employees a

nd annual sales), type

of ownership, and principal executives and biographies. http://www.dnb.com/ dbproducts/ description/ 0,2867,2- 223-1012 -0-223- 142-177- 1,00.html. We identified 60 small companies and 44 large companies with sales data . Using the data, we found 62 companies manufacture pr oducts that are included in the Pump and Pumping Equipment Manufacturing (NAICS 333911) or Air and Gas Compressor Manufac turing ( NAI CS 333912) i

ndustrie s, and 30 c ompanies manufacture pro ducts included in the Motor and Generator Manufacturing (NAICS 335312) or Welding and Soldering Equipment Manufacturing (NAI CS 333992) industries. The remaining 12 companie s manufa

cture e quipment in

both PSR seg

ments. The resu lts of the s creeni

ng ana lys is, prese

nted in Ta

ble 5-1, show that tw

o small firms have estimat

ed CSRs between 1 pe

rcent a nd 3 perc ent of sa

les and one firm ha s a CSR great er than 3 percen

t. The re maining 57 small fi

rms have estimate d CSRs below

1 perce nt. The average (median)

CSR for small firms is 0.3 percent (0.1 percent), and the avera ge and median CSR for all large firms with data is less than 0.02 percent (0.003 pe rcent). Table 5-1.Summa ry Statis tics for SBR EFA Scr eening Anal

ysis

Sma ll Large Number Sh ar e ( %) Number Sh ar e ( %) Com panies with P arent Sales Da ta 60 100% 44 100% Compliance cost s are < 1% of sal es 57 95% 44 100% Compliance cost s are $ 1 to 3% of sale s 2 3%

— 0% Compliance cost s are $ 3% of sales 1 2%

— 0% Cost-to-Sales Ra tios (%) Average 0.3% 0.0192% Median 0.1% 0.0025% Maximum 4.7% 0.2% Minimum 0.0% 0.0% 5.3 Assessm ent The RFA generally

requires an agency

to prepare a regulatory

flexibility analysis of any rule subjec

t to notice and comment rulemaking requirements under the Administrative5-5

Procedure Act or any

other statute unless the agency certifies that the rule will not have a significant economic impact on a substantial number of small entities. Small entities include small businesses, small org anizations , and smal l gover nmental ju

risdictions. The e conomic impacts of the NSPS are expected to be insignificant, and the Age ncy has dete

rmined that there is no significant impact (economic) on a substantial number of small entities (or SISNOSE).6-1 SECTION 6 HUMAN HEALTH BENEFITS OF EMISSION REDUCTIONS 6.1 Calculation of Human Health Benefits For the purposes of estimating the benefits of re ducing emissions from stationary diesel compression ignition engines through this rulemaking , EPA is using the approach and methodology laid out in EPA' s 2004 economic analy sis supporting the regulation of emissions from nonroad diesel engines (Final Reg ulatory I mpact Analysis (RI

A): Control of Emissions f

rom Nonro ad Diese l Engine s, May 2004). We c

hose this a

naly sis as the b asis since most of the elements in that rule are similar to those covered her e. Both the engine type, the controls applied, a nd the pollutants reduced are similar to those covered by the Nonroad Diesel engine rule. I n addition, EPA believes that these types of engines are broadly distribute d across

the count ry similar in d

istribution to nonroa

d diesel e ngines. Figure 6-1 shows the distribution of these engine s by State.6-26-3 These four factors lead us to believe is appropr iate to use the benefits transfer approach and values in the Nonroad Diesel e ngine rule regulatory

impacts analysis (RI

A) for estimatin g the be nefits of this rule. The bene fits tran sfer met hod used to

estimate benefit s for the final Nonroad Diesel engine r ule is similar to that used to estimate benefits in the analysis of the Large SI/Rec reational Vehicles standards (see U.S. EPA 2002, Docket A2000-01, Document VB-4). The methodology is laid out in Chapter 9 of the Nonroad Diesel engine RIA. EPA did not perform an air quality modeling assessment of the emission reductions resulting from the installation of controls on these engines due to time and r esources constraints and the limited value of that analysis for the purposes of developing the regula tory approac h. This limit

ed EPA’s a

bility to perform a bene fits anal ysi s for this rulemaking since our benefits model requires e ither air quality modeling or monitoring data. Howeve r, as men tioned ab ove, the s imilaritie s of the e ngines b eing re gulate d under th is rulemaking and those in the Nonroad Diesel rulemaking allow us to use the benefit per ton values de rived fr om the Non

road Die sel RI

A in this analy sis. To devel op the est

imate of t he bene fits of the

emission r

eduction s from this rulemaking, we used dollar benefits per ton of e missions reduced estimates for each pollutant based on the resu lts of the b enefits a

naly sis in the N onroad d

iesel RI A. We then

multiply this by the numbe

r of tons o f each p ollutant r educed to get the overa

ll benef its value. These results are summarized in Table 6-1 below. I t is important to note that the dollar benefits per ton estimates used here reflect specific geographic patterns of emissions reductions and specific air qual ity a

nd benef its modelin

g assumptions. Use

of these $/ton values to estimate benefits associated with different emission control progr ams (e.g. for reducing

emissions from large stationary

sources like EGUs) may

lead to higher or lower benefit estimates than if benefits were calculated based on dir ect air quality modeling . Great care should be taken in apply ing thes e estimat

es to emiss ion reduc

tions occ urring in any specific location , as these are all based on national or broad reg ional emission reduction programs and therefore6-4

represent average

benefits per ton over the entire U.S. The benefits per ton for emission reductions in specific locations may be very dif

ferent than the national averag e. Table 6-1: Estimate of Monetized Benefits in 2015 ($2000)* Presuming CI Engines are Spatially Distributed Similar to Nonroad Engines

$ benefits/ton Amount of

emissions reduced (tons) Monetized benefit s

Pollutant NOx $8,000 35,000 $280 million SOx $23,000 9,300 $220 million Direct PM $300,000** 2,900 $860 million Total hea lth benefit s $1.36 billion

* The results in the table are presented assuming a discount rate of three percent. Assuming a discount rate of seven percent re duces the total benefits to $1.28 billion. ** This $/ton value is higher than that found in other EPA rulemakings such as the he avy duty onroad eng

ines. This reflects the fact that nonroad engines, such as construction equipmen t, are f requent ly lo cated in

highly populate d urban a

reas an d thus more

people benefit per ton of emissions reduced. 6.2 Characterization of Uncertainty in the Benefits Estimates In any complex analysis, there ar e likely to be many

sources of uncertainty . Many inputs are used to derive the final estimate of economic bene fits, including emission inventories, air quality models (with their associated parameters and inputs), epidemiological estimates of C-R functions, estimates of values, population estimates, income estimates, and estimates of the future state of the world (i.e., reg ulations, technology, a

nd human behavior). For some parameters or inputs it may be possible to provide a statistical representation of the underlying

uncertainty distribution. For othe r parameters or inputs, the necessary information is not available.6-5 In addition to uncertainty, the annual benefit estimates presented in this analy sis are also inherently var

iable due to the truly ra

ndom processes that govern pollutant emissions and ambient air quality in a g iven year . Factors such as hours of equipment use and wea ther display constant variability regardless of our ability

to accurately

measure them. As such, the estimates of annual benefits should be viewed as repre sentative of the magnitude of benefits expected, rather than the actual bene fits that would occur every year. I n addition, as with all benefits transfer applications, the benefits per ton for spe cific emissions tons reduced will be dependent on the geogra phic location of those emissions reductions and the relative atmospheric conditions compared to the geog raphic distribution of nonroad diesel engines and the atmospheric conditions assumed in the Nonroad Diesel analy sis. Specifically, the trans fer met hod we used l eads t o an av erage ben efit p er ton that i s base d on t he aver age population exposed to emissions from nonroad engines. If popula tions exposed to ambient poll utio n resu ltin g from e miss ions from C I engines are les s dens e than the av erage population exposed to

ambient p ollution re sulting f rom nonro ad eng ines, the n the bene fits per ton associated with those emissions will be lower than that derived from the Nonroa d rule. Likewise, if populations exposed are more dense than those exposed to ambient pollution from Nonroad engines, then benefits per ton will be highe r. Above we present a primary estimate of the total benefits, based on our interpretation of the best available scientific literature and methods and supporte d by the SAB-H ES and the NAS (NRC, 2002). The benefits estimates are subject to a number of a ssumptions and uncertainties. For example, key assumptions underlying the pr imary estimate for

the prematu re morta lity which ac counts for 90 perc ent of the total benefits we

were a ble to quantify include t he follow ing: (1) Inhalation of fine pa

rticles is causally a ssociated with premature death at concentra tions near those experienced by most Americans on a daily basis. Although biological mechanisms for this effect have not y et been definitively e stablished, the weight of the available epidemiological evidence supports an assumption of causality.6-6

(2) All fine particles, regardle ss of their chemical composition, are equally potent in causing premature mortality. T

his is an important assumption, because PM produced via transported precursors emitted from EGUs may differ significantly

from direct PM released from diesel engines and other industrial sources, but no clear scientific grounds exist for supporting differential effects estimates by particle type. (3) The impact function for fine particles is approximately linear within the range of ambient concentrations under consideration. Thus, the estimates include hea lth benefits from reducing fine particles in areas with varied concentrations of PM, including both regions that are in attainment with fine particle standard and those that do not meet the standa rd. (4) The forecasts for future e missions and associated air quality modeling are valid. Althoug h recog nizing the difficul ties, assu mptions, a

nd inhere nt uncer tainties i n the over

all enterpr ise, thes e analy

ses are based on peer-r eviewe d scientif

ic litera ture and up-to-da te assessment tools, and we believe the results are highly useful in assessing this rule. The rec ent NAS re

port (NA S 2002) on e stimating public he alth bene fits of ai r pollution regulations recommended that EPA begin to move the assessment of unc ertainties from its ancillary a

nalyses into its primary

analyses by

conducting probabilistic, multiplesource uncertainty

analyses. As pa

rt of a collaboration between EPA’s Office of Air and Radiation (OAR) and the Office of Manag ement and Budget (OMB) on the Nonroad Diesel Rule, we c onducted

a pilot expe

rt elicita tion intend

ed to more

fully charac terize unc ertainty in the esti mate of mortality

resulting from expos

ure to PM. T

he final expert eli citation i s expected to be finished and peer reviewed this Fa ll. For this RIA we did not go throug h the detailed uncertainty assessment used in the Regulatory

Impact Analy

sis for the Clean Air Interstate Rule ( CAIR RIA) (March 2005) because we lack the necessa ry air qua lity input data to run the benefits model. Howe ver, the results of the Monte

Carlo ana

lys es of the

health a nd welfa re bene fits pres ented in A ppendix B of tha t RIA c an provid

e some ev idence o f the unc ertainty

surround ing the b enefits r

esults presented in this analysis.6-7

6.3 General Approach In our recent a ssessment of the Clean Air Interstate Rule (CAIR) we describe our progre ss toward

improving

our appr oach of c haract erizing the unce rtaintie s in our ec onomic benefits estimates, with particular emphasis on the concentration re sponse (C-R) function relating premature mortality to exposures to ambient PM2.5. We presented two approaches to gene rating probabil istic distr

ibutions de signed t

o illustra te the pot ential inf luence o f some aspects of the uncertainty in the C-R function in a PM benefits analysis. The f irst approach generated a probabilistic estimate of statistical unce rtainty based on standa

rd errors reported in the underlying

studies used in the benefit modeling framework. The sec ond approach used the results from the pilot expert elicitation designed to character ize certain aspects of uncertainty in the a

mbient PM2.5/mortality relationship. In addition to the two approaches to cha racterize uncertainty

for PM mortality, we incorpor ated inf ormation o

n uncert ainties of other en dpoints in the benef

its model. We

did not attempt to assign probabilities to all of the uncertain parameters in the model bec ause of a lack of resources and reliable methods. We simply genera te estimates of the distributions of dollar benefit s for PM he alth eff ects and for total dollar be nefits. F or non-mortality endpoints, we provide a likelihood distribution for the total benefits estimate, based solely on the statistical uncertainty surr ounding the estimated C-R functions and the assumed distributions around the unit values. Our estimate of the likelihood distribution for total benefits should be viewed as incomplete because of the wide rang e of sources of uncertainty that we have not incorporated. The 5th and 95th percentile points of our estimate ar e based on statistical error, and cros s-study

variabi lity

provides some insig

ht into how u

ncerta in our est imate is with regard to those sources of uncer tainty. However, it does not capture other sources of uncertainty r

egarding other inputs to the model, including emissions, air quality , baseline population inciden

ce, proj ected exposures, or the mode l itself, in cluding aspects of the he alth science not captu red in the studies, s uch as the likelihood

that PM is ca

usally related to premature mortality a

nd other serious health effects. Thus, a likelihood description based on4 Because thi s is a nati onal anal ysi s in w hich, for eachendpoint, a sin gle C-R f uncti on is appl ied every where, there are two sources of uncert ainty about i ncidence: stat isti cal uncertaint y (due to sampling error) about the true val ue of the pollut ant coeffi cient in t he location where the C-R funct ion was estimated and unce rtainty ab out ho w well an y given p ollutant c oefficien t app roxim ates ß*.
5 Altho ugh suc h a “likeliho od d istribution ” is not fo rmally a B ayesian poste rior d istribution , it is very similar in concept and f unct ion ( see, for

exampl e, t

he di scus sion

of t he Baye sian approa ch i n Kennedy (1990), pp. 168-172 ). 6-8 the standard error would provide a misleading pic ture about the overall uncertainty in the estimates. Both the uncertainty

about the incidence changes 4 and uncertainty about unit dollar
va lu es ca n b e c ha ra ct er iz ed by distributions . Each “likelihood distribution” characterizes our beliefs about what the true value of an unknown varia ble (e.g., the true chang e in incidence of a given healt

h effect in relat

ion to PM expos

ure) is li kely to be, bas ed on the a

vailable informat ion from r

elevant studies. 5 Unlike a sampling distribution (which describes the possible values that an estimator of an unknown variable might take on), this likelihood distribution describes our beliefs about what values the unknown variable itself mig ht be. Such likelihood distributions can be constructed for each underly ing unknown variable (such as a particular pollutant coefficient for a pa rticular location) or for a function of sever al underlying

unknown variables (such as the total dollar benefit of a re gulation). In either case, a likelihood distribution is a characterization of our beliefs about what the unknow n variabl e (or the

function of unknow

n variab les) is lik

ely to be, bas ed on all t

he avail able releva nt informa

tion. A like lihood des cription b ased on su ch distrib utions is ty

pically expressed as the interval from the 5th percentile point of the likelihood distribution to the 95th perc entile po int. I f all unc ertainty had bee n include d, this ra

nge wou ld be the “ credibl e ra ng e” wi th in wh ic h w e b el ie ve th e t ru e v al ue is li ke ly t o l ie wi th 90 pe rc en t p ro ba bi li ty.

6.4 Monte-C arlo Base d Uncert ainty Analy sis The uncertainty

about the total dollar benefit associated with any single endpoint combines the uncertainties from these two sources (the C-R re lationship and the valuation)6 Th is metho d assu mes tha t the incid ence chang e and the unit d ollar va lue for a n end poin t are stoc hastica lly indep end ent. 6-9 and is estimated with a Monte Carlo method. In eac h iteration of the Monte Carlo procedure, a value is randomly dr

awn from the incidence distribution, another value is randomly drawn from the unit dollar value distribution; the total dollar benefit for that iteration is the product of the two. 6 When this is repeated for many (e.g., thousands of) iterations, the distribution of total dollar benefits associated with the endpoint is generated. Using this Monte Carlo procedure, a distribution of dollar benefits can be generated for eac h endpoin t. As the nu

mber of M onte Carl o draws gets larg er and la rger, the Monte Carlo-generated distribution becomes a better and better approximation of a joint likelihood distribution (for the considered parameters) making up the total monetary bene fits for the endpoint. After e

ndpoint-s pecific distributi ons are generat

ed, the sa me Monte Ca

rlo proced ure can then b e used to com bine t he dol lar ben efits from d iffere nt (no nover lappi ng) endpoints to generate a distribution of total dollar benefits. The estimate of total benefits may be thought of as the end result of a sequential process in which, at each step, the estimate of bene fits from an additional source is added. Each time an estima

te of dolla

r benef its from a new sour ce (e.g ., a new health en

dpoint) is added to t he previ ous estima

te of tota l dollar be

nefits, the estimat

ed total d ollar be nefits increases. However, our bounding or likelihood description of where the true total value lies also increases as we add more source s. The physical ef fects estimated in this analysis ar e assumed t o occur i ndepend ently . It is

possible that, for a

ny g iven pollu

tion leve l, there

is some correlation between the occurre nce of physic al effects, due to say avoidance behavior or common causal pathways and treatments (e.g., stroke, some kidney disease, and heart attack are related to treatable blood pr essure). Estimating accurately any such cor

relation, however, is bey

ond the scope of this analy sis, and instead it is simply assumed that the ph ysi ca l e ff

ec ts oc cu r i nd ep en de nt ly. For the CAIR RI

A, we conducted two different Monte Carlo ana lyses, one based on the distribution of reductions in premature mortality characterized by

the mean effect6-10

estimate and stand

ard err or from the epidem

iology study of PM-ass ociated

mortality associa ted with the primar

y e stimate (Pope et al ., 2002), and one b ased on the results from a pilot expert elicitation project (IEc, 2004). I n both analyses, the distributions of all other health endpoints are characterized by the reported mean and standard deviations from the epidemiology litera

ture. Distributions for unit dollar values are based on reported ra nges or distributions of values in the economics literature and are summarized in Table B- 1 of Appendix B of the CAIR RI A. Summary results of the Monte Car lo analyses base d on the Pope et al. (2002) distribution and based on the pilot expert elicitation are presented in the next section. 6.5 Results of the CAIR R IA Monte

Carlo Analyses Results of

the Monte

Carlo simu

lations ar e prese nted in Ta

ble 6-2. T

he table provides the estimated means of the distributions and the estimated 5th and 95th percentiles of the dis tributions . The con tribution o f mortali

ty to the mean benefit s and to bot

h the 5th and 95th percentiles of total benefits is substantial, with mortality a ccounting for 93 percent of the me an estima

te, and e ven the 5t h perce ntile of mortality

benefit s dominatin

g the 95 th percentile of all other benefit categories. Thus, the choice of value and the shape for likelihood distribution for VSL should be examined closely and is key information to provide to decision makers for any decision involving this variable. Table 6-2: Results of M onte Car lo Uncer tainty Ass essme nt from C AIR RIA

5th perc entile mean 95 th perc entile Statis tical uncertainty

based approach $26 B ill ion $100 B ill ion $210 B ill ion

Ex pert elici tati on uncertainty

based appro ach $3 Billion $74 B ill ion $240 B ill ion6-11 For the statistica

l based a pproach , the 95th p ercent ile of tota l benefi ts is approximately twice the mean, w hile the 5 th percentile is approximately one- fourth of the mean. The overall range f rom 5th to 95th represents about one order of magnitude. F or the expert elicitation based approach, the 95th percentile of total bene fits is approximately three times the mean, while the 5 th percentile is approximately one-twentieth of the mean. The overall range from 5th to 95th is somewhat wider than that of the statistical based approach. Prior to taking the next step and applying these results to the benefits estimates from this rulemaking, it is important to note that there are numerous cavea ts and limitations associated these uncertainty approaches which are not refle cted in this write-up. Readers are strongly e ncouraged to review the deta iled discussion in Appendix B of the CAIR RIA for a detailed discussion of these

approac hes. As a means of assessing the uncertainty associated with the estimate of the monetized benefits of this rulemaking we applied the ratios from the a bove table to the benefit estimates presented earlier. Thus, to estimate the 5th perce ntile of the statistical uncertainty based approach we multiplied the ratio of the 5th percentile to the mean (26/100) by the estimated benefits of this rule ($1.36 Billion). Using these values as a g uide, we assumed that the distribution of values for this rulemaking would be similar. Table 6-3 summarizes the results.6-12

Table 6-3: Estimated Monetized Health Benefits Compared to Two Approaches for Estimating the Uncertainty Range 5th perc entile mean 95th perc entile Estimated monetized benefits** NC* $1.36 Billion NC*

Statistica l uncert ainty based approach $360 Million NC* $2.85 Billion Expert elicitation uncertainty ba sed approach $68 Million NC* $4.4 Billion

* NC - Not Calculated ** 3 perc ent disco

unt rate

6.6 Benefits By Engine Size Category We analyzed the distribution of monetized benefits acr oss engine size categories. Thi s an alys is ass um es t hat th e av era ge $ ben efi ts per to n ba sed on t he N onr oad Die sel RIA analysis is repre sentative of the average be nefits that would accrue to reductions in emissions for each individual engine size category. This introduces additional unce rtainty in the analysis. As such, this analy sis should be viewed as providing the most information when used in a r

elative s ense, e. g., com

paring relative magnitu des acr oss cate gories , rather

than in an absolu te sense , as little

confide nce shou ld be plac ed on the s

pecific estimate s of bene fits for any one cate gory

of eng ine size. A s with all b enefits t ransfe r applic ations, the benef

its per ton f or any specific groupin g of en gine size s will be de

pendent on the ge ographic location of those e

ngines a nd the re lative at

mospheri c conditi ons compa red to the geog raphic distribution of nonroad diesel engines and the atmospheric conditions assumed in the Nonroad Diesel a naly sis.6-13 The results of this analysis shown below in Ta ble 6-4 suggest that there are net benefit s for eve

ry engine size cate gory . In a

ddition, the benef

it-cost r atio exce eds 8 for

all engine size categories, althoug h the benefit-cost ratios tend to be larger f or the larger engine size cate gories . It should be no ted that the impacts listed in Ta

ble 6-4 a re assoc iated wit

h prime engines. We believe that emerg ency e ngines, which make up about 80 percent of the engine s covere d by

this rule, will have

minimal inc

rementa l costs an d benefi ts. We assu

me that emergency

engines will be compliant with certain non-road diesel re quirements, thereby meeting the requirements of this rule.6-14 Table 6-4. Benefits of Emission Reductions in 2015 by Engine Si ze Category

Em ission Reducti ons by Pollutant (t

ons) Be nefits (3% , million 2000$) Be nefit s (7% , million 2000$) Annualize d C osts (2000$) Net

Be nefits Net

Be nefits Benefits/

Co sts Benefits/

Co sts

Engine Size( hp) PM 2.5 NOx SO 2 3% 7% 3% 7%

50-75 64

- 270 25.3 23.8 2.9 22.4 21.0 8.8 8.3
75-100 324 1620 542 112 .0 114 .8 6.5 105 .5 108 .4 17.3 17.8 100-175 498 2780 1184 181 .0 186 .5 11.0 170 .0 175 .5 16.5 17.0
175-300 738 5044 1983 275 .4 288 .6 12.5 262 .9 276 .0 22.0 23.0 300-600 497 3345 1437 187 .8 196 .2 6.7 181 .0 189 .5 27.9 29.2
600-750 101 673 372 40.0 41.6 1.2 38.8 40.4 32.5 33.8 750-1200 187 1069 997 80.9 82.4 3.8 77.1 78.6 21.2 21.6
1200- 3000 399 20421 2116 210 .5 315 ..3 10.8 199 .7 304 .5 19.4 29.1

>3000 62 3210 351 33.3 49.8 1.6 31.7 48.1 20.3 30.36-15 6.7 Benefit-Cost Comparison Recall that the costs of this rulemaking are estimated to be $57 million in 2015. Regardless of the uncertainty characterization approach used, the bene fits of this rule exceed the costs, even when the cost is compared to the 5th perce ntile estimate of the expert elicitation approach. Thus, EPA believes that the benefits of this rulemaking will exceed the costs. The reader should refer to the CAI R RIA for a full detailed discussion of the uncerta inties con sidered

in EPA’s be

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