POST-CLOSURE LIABILITY TRUST
FUND SIMULATION MODEL
VOLUME I: MODEL OVERVIEW AND RESULTS
Office of Solid Waste
U.S. Environmental Protection Agency
May 1985
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TABLE OF CONTENTS
VOLUME I: MODEL OVERVIEW AND RESULTS
PREFACE i
1. INTRODUCTION 1-1
-f
1.1 Summary of CERCLA Post-Closure Provisions 1-2
1.2 Overview of Approach 1-3
2. MODEL OVERVIEW 2-1
2.1 Model Design Objectives 2-1
2.2 Type of Model Chosen < 2-2
2.3 Factors Included in the Model 2-3
2.3.1 Basic Model Units 2-6
2.3.1.1 Facility Population 2-6
2.3.1.2 Facility-Level Characterization 2-8
2.3.1.3 Release Types Modeled 2-14
2.3.1.4 Actions and Claims 2-18
2.3.1.5 Funding Sources 2-22
2.3.2 Relationships Among the Basic Model Units 2-23
2.3.2.1 Economic Relationships 2-23
2.3.2.2 RCRA Policies 2-24
2.3.2.3 Financial Relationships 2-26
2.3.2.4 Allocation Policies 2-27
2.3.2.5 Legal Validity of Claims 2-30
2.3.2.6 The Effect of Response Actions on Releases
(Physical Relationships) 2-30
3. HOW THE MODEL IS USED TO ASSESS PCLTF ADEQUACY 3-1
3.1 Use of the PCLTF Simulation Model 3-1
3.2 Interpretation of the Model Results 3-2
3.3 Assessing PCLTF Adequacy 3-5
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TABLE OF CONTENTS (continued)
Page
4. SIMULATION RESULTS 4-1
4.1 Simulation 1 4-1
4.1.1 Definition of Simulation 1 4-1
4.1.2 Simulation 1 Results 4-4
4.1.3 PCLTF Adequacy in Simulation 1 4-12
4.2 Policy Simulations 4-15
4.2.1 Definition of the Four Policy Simulations 4-15
4.2.2 Policy Simulation Results 4-17
4.2.3 Distribution of Costs 4-18
4.3 Conclusions 4-21
5. REVENUE ANALYSIS 5-1
5.1 Introduction 5-1
5.2 Definition of the Revenue Analysis 5-2
5.2.1 Defining the Revenue Policy 5-2
5.2.1.1 Fund Floor and Ceiling Rules 5-2
5.2.1.2 Tax Rules 5-3
5.2.1.3 Recoverability of Fund Expenditures 5-4
5.2.2 Revenue Analys is Outputs 5-4
5.3 Simulated Revenue Policy 5-5
5.4 Results of Revenue Policy Simulations 5-6
5.4.1 Overview of Results 5-6
5.4.2 Break-Even Tax Rate Analysis Results 5-9
5.5 Concluding Note 5-9
6. MAJOR LIMITATIONS AND NEXT STEPS 6-1
6.1 Key Assumptions Affecting Estimates of PCLTF Adequacy 6-1
6.2 General Modeling Limitations 6-3
6.3 PCLTF Simulation Model 6-4
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TABLE OF CONTENTS (continued)
6.4 Release Simulation Model 6-8 •
6.4.1, Release Simulation Simplifying Assumptions 6-8
6.4.2 Release Simulation Data Limitations 6-10
6.5 Next Steps 6-12
VOLUME II: GRAPHS AND TABLES OF MODEL RESULTS
VOLUME III: APPENDICES
Appendix A: Detailed Model Description and Data Analysis A-l
A. 1 Model Overview A-2
A.2 Estimation of Model Units A-4
A.2.1 Facility Population A-4
, A.2.1.1 .Existing Facility Population A-4
A.2.1.2 New Facility Population A-12
A.2.2 Facility Level Characterization A-12
'A.2.2.1 Facility Milestones A-13
A.2.2.2 Facility Attributes A-28
A. 2.3 Modeling of Releases A-53
A.2.4 Actions A-63
A. 2 .4.1 Monitoring Act ions A-64
A.2.4.2 Response Actions A-70
, A.2.4.3 Post-Closure Care A-76
A.2.5 Claims A-80
A.2.5.1 Personal Injury Claims A-81
A.2.5.2 Real Property Damage Claims A-98
A.2.5.3 Economic Loss Claims A-102
A.2.5.4 Natural Resource Damage Claims A-103
A.2.6 :Funding Sources A-107
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TABLE OF CONTENTS (continued)
Page
A. 3 Relationships Among the Model .Units A-111
A.3.1 Economic Relationships A-113
A.3.1.1 Relationship Between the Demand for Land
Disposal Capacity and the Facility
Population A-114
A.3.1.2 Relationship Among Fund Balance, Revenues,
and Spending A-122
A.3.2 Regulatory Policy A-125
A.3.2.1 Permit Policy A-125
A.3.2.2 Action Policy : A-128
A.3.3 Financial Relationships A-135
A. 3.3.1 Background Rate of Bankruptcy A-138
A.3.3.2 Relationships Between the Owner/Operator's
Financial Characteristics, Expenditures, and
Likelihood of Bankruptcy A-138
A.3.4 Cost Allocation Policy A-140
A.3.4.1 Qualification Policy A-140
A.3.4.2 Allocation of Costs to Funding Sources A-142
A.3.5 The Effect of Response Actions on Releases A-147
A.3.6 Legal Validity of Claims A-153
Appendix B: Release Model B-l
B.1 Model Methodology B-l
B. 1.1 User Supplied Inputs and Default Values B-l
B. 1.2 Internally-Stored Inputs B-7
B.I.3 Relationships and Functions B-16
B.2 Outputs B-27
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TABLE OF CONTENTS (continued)
B.3 Discussion B-29
B.3.1 Low-Probability Events B-29
B.3.2 Land Treatment Facilities B-29
B.3.3 Injections Wells B-29
B.3.4 Corrective Actions B-30
Appendix C: Model Implementation C-l
C.I PCLTF Simulation Model C-l
C. 1.1 Run Characteristics C-l
C. 1.2 Pseudo-Random hiumber Generator C-l
C.I.3 Statistical Properties of Results C-6
C.I.% Subroutines C-10
C.I.5 Data Bases C-14
C.2 Release Model C-14
C. 2.1 Run Characteristics C-14
C.2.2; Pseudo-Random Number Generator C-15
C.2.3 Subroutines C-15
C. 2.4 Data Bases C-17
Appendix D: User Options D-l
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PREFACE
This report is the joint effort of two co-contractors, ICF Incorporated
and-Battelle Pacific Northwest Laboratories. Battelle Pacific Northwest
Laboratories developed the Release Simulation Model, a model that predicts
facility releases. ICF Incorporated developed the overall Post-Closure
Liability Trust Fund Simulation Model, which uses the results of the Release
Simulation Model to assess the adequacy of the PCLTF.
This report is organized in three volumes, as follows.
Volume I, "Model Overview and Results," introduces the Post-Closure
Liability Trust Fund and presents background information on the RCRA program;
describes in general terms the PCLTF Simulation Model and how it is used;
presents Model results for five simulations; presents an analysis of the
revenue requirements under alternative Fund coverage policies; and presents a
discussion of necessary limitations and next steps.
Volume II, "Graphs and Tables of Model Results," presents the graphs and
tables that contain the Model results discussed in Volume I. Volume II is
separately Bound so that the reader can conveniently peruse the Model output
when reading the Volume I report.
Volume III, "Appendices," contains four appendices that provide technical
detail on various aspects of the Model. Appendix A presents detail on
methods, data and assumptions used in the PCLTF Simulation Model. Appendix B
provides detail on the Release Simulation Model. Appendix C describes several
aspects of model implementation for both the PCLTF Simulation Model and
Release Simulation Model, including run characteristics, subroutines, data
files, random number generators, and statistical properties of the results.
Appendix D reproduces the set of options available to the Model user.
. This study, was conducted under the guidance of EPA's Office of Solid
Waste. Ms. Carole Ansheles, the Project Officer, and her associate Mr.
Richard Allan were in charge of this project. Mr. Peter Guerrero was the
former Project Officer.
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CHAPTER 1
INTRODUCTION
The Comprehensive Environmental Response, Compensation, and Liability Act
of 1980 (CERCLA) provides for two complementary funds to address problems
posed by inactive hazardous waste sites:
• The Hazardous Substance Response Fund, established by
Section 221, to be used for the clean-up of sites that
are currently inactive; and
• The Post-Closure Liability Trust Fund, established by
Section 232, to be used for response, care, and
compensation in the future for disposal sites that are
currently active.
Each fund serves a different purpose and has different financing
arrangements. The Response Fund (often referred to as "Superfund") is used
for current problem sites and is funded mostly by a tax on feedstocks to the
chemical production process. The Post-Closure Liability Trust Fund (PCLTF) i
funded by a tax on hazardous waste disposal and is designed to address
long-term problems at permitted hazardous waste land disposal facilities.
Because of the .many uncertainties about the provisions and implementation of
the PCLTF, Congress mandated that several key analytical studies be performed
The work represented by this report was motivated by one of these
congressionally-mandated studies: an analysis by the Environmental Protectio:
Agency of the adequacy of the revenues to be raised for the PCLTF in relation
to its estimated future requirements. The purpose of this effort by ICF
Incorporated and the Battelle Pacific Northwest Laboratories is, therefore, t<
assist EPA in preparing the analysis.
An analysis of Fund adequacy must be based upon a projection of inflows
and outflows of funds that will occur over a very long period of time. This
report presents a model of these flows and the results of analyses undertaken
using the Model. These results are not final. The model on which they are
based will undergo further testing, revision, and inclusion of new data on thi
basis of which new estimates of Fund adequacy will be prepared. This
analytical effort can also assist EPA in other related studies as well as in <
variety of regulatory policy analysis projects.
The remainder of this chapter is organized as follows. Section 1.1
discusses key statutory provisions relating to the Fund and Section 1.2
provides an overview of our approach to this phase of overall model
development.
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1.1 SUMMARY OF CERCLA POST-CLOSURE PROVISIONS
The post-closure provisions of CERCLA were designed to:
(1) finance, at closed disposal facilities, long-terra
post-closure monitoring, maintenance, and cleanup
necessary to protect human health and the environment;
and
(2) assume fully the liability of owners and operators for
any damages to third parties incurred after site
closure, and to pay the costs of those liabilities.
A facility must meet three requirements in order to take advantage of
these provisions. First, the disposal facility must have received a final
permit under Subtitle C of the Resource Conservation and Recovery Act (RCRA).
Facilities that close while they are still in interim status will not be
covered by the PCLTF.1 Second, the facility must have complied with each
condition of its permit and applicable RCRA regulations. These include
closure and post-closure requirements as well as design and performance
standards (e.g., requirements for liners).2 Finally, the facility and its
surrounding area must be monitored for up to five years after closure to
demonstrate that there is "no substantial likelihood" that hazardous
substances will be released from confinement (or migrate off site) or that
"other risk to public health or welfare" will occur (see Section 107(k)(l)).
These three requirements were intended to limit PCLTF coverage to those
sites which have fully complied with RCRA standards and have a proven record
of performance. This limiting of fund coverage to "low-risk" sites had been
expected to reduce the likelihood that the PCLTF would be significantly
depleted by a single incident. Furthermore, those who drafted the PCLTF had
hoped that the existence of a fund to pay future damage claims and to clean up
releases would aid in the siting of new hazardous waste disposal facilities by
helping to assuage concerns over long-term care and compensation after
facility closure.3
The PCLTF is to be financed by a tax that is fixed over time at $2.13 per
dry-weight ton of hazardous waste received at disposal facilities. The tax
went into effect on October 1, 1983; it is only collected when the
(unobligated) balance of the Fund does not exceed $200 million in the previous
year. The tax may be subject to the Act's sunset provision.
1 Such facilities may fall under the jurisdiction of CERCLA's Hazardous
Substance Response Fund.
2 See EPA, "Hazardous Waste Management System; Permitting Requirements
In Land Disposal Facilities," 47 FR 32274.
3 See Environmental Emergency Response Act, S. Rep. No. 96-848 (S. 1480),
96th Cong., 2d Sess., 1980, pp. 90-91.
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CERCLA specifies how PCLTF funds may be used. Apart from some specific
issues of legal interpretation," funds may be used to pay for the costs of:
• monitoring, response, and cleanup;
• assessing damages to natural resources;
• restoring or replacing natural resources;
• assessing health effects;
* "other compensation for injury or loss under ... any
other State or Federal law"; and
• routine care and maintenance incurred by parties other
than facility owners or operators after a defined period
(i.e., 30 years).
1.2 OVERVIEW OF APPROACH
Any assessment of the adequacy of the PCLTF must take into account two key
facts:
(1) The demands on the PCLTF are uncertain because the
current estimates of the likelihood and timing of
events that will result in Fund expenditures are very
uncertain (e.g., releases from facilities that gain
eligibility for the Fund).
(2) The supply of funds to the PCLTF is uncertain because
it is a function of the amount of future hazardous
waste land disposal.
Thus, it is essential to design a model that can account for these
uncertainties. : This was done by constructing a model based on principles of
probability and statistics that can simulate particular aspects of the
hazardous waste situation which are highly uncertain. This approach, referred
to as stochastic (or Monte Carlo) simulation, permits the user to see the
effects of uncertainty on estimates of the future Fund balance.
Because of the great uncertainties involved in projecting future waste
disposal and future facility releases, and because new and better information
is continually becoming available, EPA sought a model that would: (1) make it
possible to test both the effects of various alternative assumptions regarding
technical and policy matters and the effects of alternative data bases; and
(2) be easy to update and change as new information becomes available and as
* For an analysis of these issues, see IGF Incorporated, "Planning for
the Post-Closure Liability Trust Fund," a report submitted to the Office of
Solid Waste, U.S. Environmental Protection Agency, November 1982.
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the policy process deals with new issues. EPA's goal in developing this model
is, in addition to examining the adequacy of the PCLTF, to create a tool that
can be used to enlighten the debates that inevitably surround complex
regulatory issues.
This modeling effort was designed to proceed in discrete stages. The
first stage was a one-month feasibility and preliminary design study.5 The
second stage had the goal of producing a simplified, operating model. The
results of the second phase were reported in January 1983.s Comments on the
model methodology and data were received from a five-member peer review group
and other EPA personnel.7 Based on the reviewers' comments and insights
gained from the results of the simplified model, priority areas for model
enhancement and additional data analyses were defined. During the third
stage, numerous refinements to the model were implemented in order to
represent more closely the costs that would arise at hazardous waste disposal
facilities as the result of the detection of releases into groundwater.
Modifications were made to reflect the Agency's new land disposal
regulations8 and to improve the estimates of potential third-party claims.
Data on the existing population of hazardous waste disposal facilities were^
significantly improved through the incorporation of information collected by
EPA in the National Mail Survey of RCRA-Regulated Hazardous Waste Handlers and
in the EPA-sponsored Telephone Verification Survey.
The purposes of this report are:
• to describe the revised Model developed in this third
stage of the study; and
• to present estimates of the PCLTF balance over time
under various assumptions about EPA policies, Fund
policies, and other relevant factors.
It should be emphasized that numerous EPA policies regarding the implementation
of the PCLTF have yet to be established, and that the policy assumptions used
in this report do not necessarily reflect current or future Agency policies.
Alternative policy assumptions can alter the estimates of the future PCLTF
5 ICF Incorporated, "Preliminary Design of a Computer Model for the
Post-Closure Liability Trust Fund," prepared for the Office of Solid Waste,
U.S. Environmental Protection Agency, January 1982.
6 ICF Incorporated and Battelle Pacific Northwest Laboratories,
"Post-Closure Liability Trust Fund Simulation Model: A Draft Report on Model
Methodology and Illustrative Results," prepared for the Office of Solid Waste,
U.S. Environmental Protection Agency, January 1983.
7 ICF Incorporated, "Report on Peer Review of Post-Closure Liability
Trust Fund Simulation Model," February 18, 1983.
* 40 CFR 264, Subparts F, M and N and revisions to Subparts K and L.
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balance, sometimes significantly. Consequently, the results presented here
must be viewed as contingent on the assumptions upon which they are based.
This report was drafted without continually including caveats regarding
assumptions and data limitations. Chapter 6 enumerates many of the more
important assumptions and data limitations of the current version of the
Model. Because it is as important to understand the limitations of models as
it is to understand their results, Chapter 6 is an essential part-of the
report.
The remainder of this report is organized as follows:
• Chapter 2, Model Overview, provides an overview of the
entire Model, including definitions of key terms,
identification of factors included in the Model, and
descriptions of the relationships among key Model
factors.
• Chapter 3, Model Use, describes how the Model is used
and interpreted.
* Chapter 4, Simulation Results, presents the results of
five s imu1at ions.
" Chapter 5, Revenue Analysis, presents estimates of the
revenue needed to maintain Fund solvency under various
Fund coverage policies.
* Chapter 6, Major Limitations and Next. Steps,
highlights the major uncertainties and assumptions
embedded in the Model as well as the potential of
further analysis to improve the Model.
4.
Volume II presents graphs and tab'les of the results that accompany Chapter
Volume III describes the technical details of the Model and includes the
following appendices:
• Appendix A, Detailed Model Description and Data
Analysis, provides details on the data and methods used
in the Model. Each factor included in the Model is
defined and the data used to estimate each factor are
presented.
• Appendix B, Release Simulation Model, provides details
on the data and methods used to simulate releases.
• Appendix C, Model Implementation, briefly describes
the run characteristics of the Model, its space
requirements, and the pseudo-random number generators
used to drive the stochastic nature of the Model.
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Appendix D, User Options, provides a copy of the
inputs to the Model over wbicb tbe user can easily
exercise control. The values displayed in this appendix
were used in Simulation 1 presented in Chapter 4.
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CHAPTER 2
MODEL OVERVIEW
This chapter presents a non-technical overview of the PCLTF Model.
Section 2.1 describes the design objectives for the Model and Section 2.2
describes the type of model chosen. Section 2.3 presents, in simplified
terms, the basic "building blocks" of the Model, The building blocks are the
lowest level or most disaggregated components of the Model. By identifying
these fundamental building blocks and the relationships among them, the
complex Model analyses are broken down into simplified parts. Section 2.3.1
describes the most disaggregated components of the Model, called Basic Units.
Section 2.3.2 'describes the relationships among the Basic Units.
Although this chapter identifies and defines the Basic Units and
relationships, it does not describe in detail the manner in which they are
estimated. A more detailed and technical description of the Model is
presented in Appendices A and B, including a full description of the data
analyses and methods underlying the various components of the Model. The
reader may selectively refer to these appendices for details on individual
components of interest.
2.1 MODEL DESIGN OBJECTIVES
In order for the PCLTF Model to help enlighten the debates surrounding
various PCLTF regulatory issues, the Model itself had to remain as
uncontroversial as possible. Consequently, the primary objective in the
design and implementation of the PCLTF Model was to develop an unbiased and
neutral tool for examining the adequacy of the PCLTF.
To attain the status of a neutral analytical tool, a model was needed
that could incorporate a wide range of possible assumptions regarding
regulatory policies, physical phenomena, legal issues, and economic
conditions. By. developing and using such a model, the debates and analyses
could appropriately center on the choice and estimation of key assumptions and
input factors, while the model itself would provide an internally consistent
means of discovering the implications for PCLTF adequacy of each set of
assumptions or input estimates.
A second objective for the design of the model was that it incorporate
and reflect the'.uncertainty surrounding many aspects of the PCLTF adequacy
question. Many facets of the analyses necessary to assess the adequacy of
the PCLTF are subject to very large uncertainties. These uncertainties arise
due to the scarcity of relevant data and, in some cases, an incomplete
understanding of important physical processes. Because the potential
uncertainties are so large, their inclusion and, where possible, their
quantification, were important design considerations for the PCLTF Model.
Finally, the design of the model must allow for the incorporation of
relevant new data and analyses that will become available over time. From the
outset, it was recognized that a substantial research effort could be devoted
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to each of a large number of critical model components. Much of this research
is currently ongoing or anticipated in support of other RCRA regulatory
analyses. Consequently, a model design was required that would facilitate
enhancement andrevision. In this manner, the PCLTF Model could be kept
up-to-date with the most recent EPA research and analysis results.
2.2 TYPE OF MODEL CHOSEN
To attain the objectives described in the previous section, a model was
chosen and designed with the following major characteristics:
* a large set of options is provided for users to
specify various assumptions and inputs;
• the Model operates as a stochastic simulation; i.e.,
key aspects of the Model are simulated using probability
distributions; and
• the Model is structured into independent segments,
which simplifies revisions and enhancements.
An easy-to-use set of options enables the Model user to reflect
assumptions regarding Fund policies, RCRA regulatory policies, disposal
facility characteristics, costs, release rates, and numerous other factors.
The Model can easily produce estimates of PCLTF adequacy for a wide variety of
combinations of assumptions and inputs. This ability helps maintain the
neutrality of the Model as an analytic tool because different estimates of the
adequacy of the PCLTF should be traceable to specific assumptions regarding
various factors influencing-Fund expenditures. Appendix D presents the full
set of options available to the user for choosing input parameters.
The Model was developed as a stochastic simulation in order to reflect the
large uncertainties surrounding various aspects of the analysis. The PCLTF
Model is a stochastic or probabilistic model (often referred to as a "Monte
Carlo" model) which is based on principles of probability and statistics. The
Model produces output in the form of a distribution of potential outcomes.
The distribution represents the combination of the various uncertainties in
all the input factors, and can be interpreted by using standard statistical
methods. Consequently, rather than provide a point estimate of the Fund
balance such as an expected value or average, the Model produces a range of
possible Fund balances. The distribution is created by performing a series of
independent repetitions within a single Model run. Each repetition, called an
iteration, performs an entire analysis of the PCLTF. The results of the
iterations form the distribution of outcomes for a single Model run.1
1 The choice of the number of iterations to perform in a single Model
run depends on the statistical properties of its results as well as the
resources available for computer use. The statistical properties of the Model
results are discussed in Appendix C.
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The importance of being able to reflect the uncertainties associated with
future PCLTF adequacy cannot be overemphasized. The uncertainties inherent in
current estimates of many important factors (such as future rates of hazardous
waste disposal, legal regimes in various states, costs of hazardous waste
treatment and disposal, corrective action costs, etc.) and the probabilistic
nature of various physical phenomena make single-value estimates very
misleading. Additionally, the complexity of the relationships among the
numerous factors (each of which is somewhat uncertain) militates against a
purely analytic approach for providing a realistic assessment of future Fund
expenditures and revenues.2 Therefore, the stochastic simulation approach
used in the design and implementation of the PCLTF Model is an important
characteristic which adds considerably to the value and reliability of its
results.
Finally, to facilitate the Model enhancement and revision, it has been
given a highly structured form. The Model is comprised of a large number of
semi-independent segments, with each segment having a particular purpose.3
If one segment requires revision (e.g., a new approach becomes available for
estimating the costs of responding to a release), the remainder of the Model
may remain unchanged. This ability is particularly valuable because
considerable research and analysis on topics relating to PCLTF adequacy are
.currently being undertaken in support of EPA's RCRA regulatory process.
Consequently, the value of the Model is enhanced by its ability to be updated
in a reasonable amount of time to reflect the most current Agency data and
analyses. '
2.3 FACTORS INCLUDED IN THE MODEL
This section provides an overview of those elements of the hazardous waste
land disposal situation that are considered in the PCLTF Model analysis. The
information is presented in a non-technical manner, although a general
familiarity with current EPA RCRA requirements is assumed. For more detailed
descriptions of the Model data and methods, the interested reader is referred
to Appendices A and B (Volume III).
To organize the presentation of this complex and interrelated material,
the factors included in the Model are divided into two types: Basic Units and
relationships among Basic Units. Exhibit 2-1 displays a simplified
representation of these two types of Model factors. The Basic Units
(represented in the exhibit with rectangular boxes) include:
* Facility Population;
• Facility-Level Characterization;
2 An analytic approach would involve representing all the factors
influencing the Fund's balance in a series of equations, and solving these
equations for an expression equal to the Fund balance over time. While
conceptually possible, this approach is not feasible on a practical level.
3 Appendix G lists the Model segments and their purposes.
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EXHIBIT 2-1
SIMPLIFIED REPRESENTATION OF THE PCLTF SIMULATION
MODEL ANALYSIS1
LAND DISPOSAL
FACILITY
POPULATION
OVER TIME
ECONOMIC
RELATIONSHIPS
RELEASE
DATA
FACILITY-LEVEL CHARACTERIZATION:
• Characteristics
• Releases
• Monitoring Actions
• Response Actions
• Claims
FINANCIAL
RELATIONSHIPS
PHYSICAL
RELATIONSHIPS
FUNDING
SOURCES
^ ^
"* /^ALLOCATION
V, POLICIES
1 Rectangles display Basic Model Units. Ovals display the relationships
among the Basic Model Units.
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• Release Data;
• Action and Claim Data; and
• Funding Sources.
The values estimated for each of these Basic Units are influenced by the
relationships ,; among them (represented by ovals). These include:
• Economic Relationships;
• RCRA Policies;
• Financial Relationships;
• Physical Relationships;
• Legal Issues; and
• Allocation Policies.
As displayed in Exhibit 2-1, the PCLTF Model analysis proceeds as follows:
• The estimate of the population of land disposal
facilities over time is based on economic relationships
describing the supply of and demand for the land
disposal of hazardous waste.
• For each facility in the facility population, a
facility-level characterization is performed to estimate
a series of characteristics (such as facility size and
design). Based on these characteristics and the release
data, the facility's potential releases are estimated.
" The estimated releases at a facility are an important
driving influence on the facility-level characterization.
In" conjunction with RCRA policies and physical relation-
ships, the estimated releases lead to monitoring
actions, response actions, and claims. The costs of
these actions and claims are estimated using the actual
and claim data.
* The viability of the firm that owns the facility is
dependent on the financial relationships among the
firm's net worth, total assets, and expenditures for
monitoring and response actions and claims. A firm's
bankruptcy will lead to the closing of its operating
facilities and the allocation of incurred costs to other
funding sources.
* The costs of the actions and claims estimated at the
facility level are allocated to the available funding
sources according to specified allocation policies. The
PCLTF is one of the potential funding sources available
to cover costs that arise at the facility level.
Various legal issues regarding the validity of
third-party claims also play a role.
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• An important feedback between funding sources and the
facility-level characterization exists. If no funding
source is available for a monitoring or response action,
then the action is not taken. This feedback is
important because a response action can prevent
additional releases from occurring at a facility.
Consequently, not having a funding source for certain
actions may lead to additional numbers of releases.
The following two subsections describe the Basic Units and relationships
included in the PCLTF Model. These sections are organized according to
Exhibit 2-1.
2.3.1 Basic Model Units
2.3.1.1 Facility Population
The population of land disposal facilities considered in the PCLTF
Simulation Model includes those facilities with the following land disposal
processes:
• surface impoundments (storage, treatment, and
disposal);
• landfills;
• land treatment; and
• injection wells.
An individual facility may employ one or more of these four processes.
Facilities that handle hazardous waste but do not employ at least one of these
processes are not considered.
The initial facility population (i.e., the facility population as of
October 1, 1983, which is the beginning of year 1 of the PCLTF Model) is
estimated using the most recent data describing interim status facilities:
• Part A Permit Application Data (1979-1980), which
identify approximately 2,600 land disposal facilities;
• Telephone Verification Survey Data (1982) for
approximately 1,170 of the 2,600 facilities identified
in the Part A data; and
• National Mail Survey of RCRA-Regulated Hazardous Waste
Handlers Data (1983), which provide the best estimates
of the current aggregate facility population and
additional detailed information on a small number of
land disposal facilities.
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2-7
Each of these data sources has strengths and weaknesses. No one source
provides a fully comprehensive and verified listing of facilities that
currently operate one or more of the land disposal processes analyzed in the
Model. Therefore, the three sources were combined to form a data base from
which the current facility population is estimated.
To reflect the current uncertainty in the true size of the facility
population, the population is estimated probab ilistically. This means that
the number of existing interim status facilities simulated in each iteration
of a Model run .will vary. Because this variance may influence the PCLTF
balance, the Model results reflect the uncertainty surrounding the size of the
facility population.
The Part A 'Permit Application data form the pool of facilities from which
the current facility population is estimated. Each facility in this large set
(2,600) can potentially be simulated as an existing facility. The following
procedure is performed for each facility listed in the Part A Data:
• If Telephone Verification Survey (TVS) data on the
facility are not available (as is the case for over
1,400 facilities), then a TVS response for the facility
identifying the disposal processes present is simulated
using an estimated relationship between the Part A Data
and TVS data. This relationship was estimated based on.
the 1,170 facilities for which both Part A and TVS data
are available.
*.
• The facility's TVS data (simulated or actual) are
adjusted based on an estimated relationship between the
TVS data and the Mail Survey results.
• The adjusted TVS data identify whether the facility
is; simulated to have one or more of the disposal
.processes included in the Model. If none of the
disposal processes is simulated to exist at the
. facility, then the facility is dropped (for that
iteration of the Model run). Otherwise, the facility
becomes part of the existing facility population.
Because the adjustments and simulations in this process are based on
probabilities, the simulated number of facilities (generated with the use of
pseudo-random numbers) will vary. The expected value (i.e., the average) of
the number of facilities and processes in the existing facility population is
as follows:
• 769 surface impoundments;
• 198 landfills;
• 68; land treatment processes;
• 90j injections wells; and
• 980 facilities.using one or more of the above
ii processes.
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2-8
These estimates agree closely with preliminary estimates recently published by
EPA."
New facilities are added to the Model's facility population over time as
additional capacity is expected to be required to meet the demand for land
disposal of hazardous waste. The future demand for hazardous waste land
disposal is very uncertain. Proposals are currently being considered to ban
or restrict the land disposal of hazardous waste in the near future. Whether
these proposals are enacted will influence the future demand for land
disposal, and consequently, the estimate of the size of the future population.
The Model is capable of assessing the implications of a variety of future
land disposal scenarios. Of note is the fact that estimates of potential
PCLTF expenditures over the next 100 years are not sensitive to assumptions
regarding the number of new facilities created over the next 100 years.5
This is because within this time frame, PCLTF expenditures are driven almost
exclusively by the costs that arise at existing facilities.
2.3.1.2 Facility-Level Characterization
Each facility in the facility population is characterized in detail within
the Model. Because detailed facility-specific data are not available for most
facilities, the values for the characteristics are simulated. These
simulations are based on descriptors of the distribution of facility
characteristics within the facility population. For example, based on the 44
landfill responses to the Mail Survey, the distribution of landfill size (in
acres) in the existing facility population can be estimated. This
distribution is used to simulate the sizes of existing landfills within the
Model. The size distribution of landfills simulated to open in the future
may, of course, differ from the distribution for existing landfills.
Consequently, a hypothetical size distribution for new landfills is required,
which cannot (by definition) be based on actual observations of new landfill
s izes.
It is important to differentiate between the facility-level
characterization discussed here and the performance of facility-specific
analyses. An alternative to simulating facility characteristics (as is
currently performed in the Model) is to collect facility-specific data for all
facilities. Such data could be used to analyze each facility and produce
facility-specific estimates of future facility performance.
4 See: "National Survey of Hazardous Waste Generators and Treatment,
Storage and Disposal Facilities Regulated Under RCRA in 1981: Preliminary
Highlights of Findings," U.S. Environmental Protection Agency, August 30, 1983.
5 It should be noted, however, that if the PCLTF funding source remains
a waste-end tax, then potential future PCLTF tax revenues are very sensitive
to the future land disposal scenario adopted.
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2-9
Because of the time and resource requirements of collecting site-specific
information, these data were not obtained for this analysis. Consequently,
although data on actual facilities are utilized in the Model, the Model
results are not site-specific.6 The aggregate performance across all the
facilities is,' however, respresentative of the overall future performance of
land disposal ."facilities. Therefore, although site-specific facility
performance is, not evaluated, the analysis is appropriate for examining the
adequacy of the PCLTF and other questions.
The characteristics simulated at the facility level can be divided into
two types: milestones and attributes. Milestones are activities that every
facility must perform at some point in time. For example, every facility must
eventually close (i.e., cease accepting hazardous waste for land disposal).
Milestones are'simulated at the facility level by estimating when they will
occur. Attributes, on the other hand, describe the facility at a given point
in time. For example, the size of a landfill is a facility attribute. The
size is simulated by estimating a particular value, e.g., two acres.
The milestones, and the bases upon which they are simulated in the Model
at the facility level, are the following:
* Begin Handling Hazardous Waste for J^and Disposal:
based on Telephone Verification Survey and Part A data
identifying when existing facilities began operation.
New facilities are simulated to begin operation as
required to meet the demand for land disposal.
* Apply for a Final RCRA Permit: based on the assumed
rate at which EPA will require and review permits at
existing facilities. New facilities begin operation
with permits.
I
* Cease Accepting Hazardous Waste for Land Disposal:
based on an estimated distribution of facility life
developed from 174 responses to the Mail Survey. The
simulated facility lifetime is added to the year in
which land disposal is simulated to have begun in order
to estimate the time of facility closure. This
milestone defines the beginning of the post-closure
period. Very small facilities may excavate and
decontaminate their land disposal process at this time,
thereby avoiding any post-closure requirements.
6 The limitation on the ability to perform site-specific analyses is
primarily caused by the lack of detailed site-specific data. However, the
methods used to assess future facility performance can be used with most of
the potential site-specific data, thereby providing site-specific estimates.
In some situations, site-specific data may fall outside the range of allowable
values that were used as the basis of the methods for evaluating site
performance. In these cases, site-specific estimates would not be possible.
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• Assess Eligibility for PCLTF Coverage: five years
after each (permitted) facility ceases accepting
hazardous waste for land disposal, a determination is
made regarding PCLTF coverage. The test used to
determine eligibility is described below in section
2.3.2.4, Allocation Policies. Facilities without final
RCRA permits do not have an opportunity to qualify for
Fund coverage.
• Owner/Operator Stops Performing Routine Post-Closure
Monitoring and Care: based on an assessment of the
adequacy of the owner/operator's (0/0's) financial
assurance for post-closure care. By regulation, the 0/0
is responsible for 30 years of post-closure care.
However, the 0/0 may, in some cases, stop performing
this care before this time due to inadequate financial
resources.
• End of the Post-Closure Care Period: the owner/
operator is responsible for the routine monitoring and
care of the facility for 30 years following closure.7
The expiration of this period represents a change in 0/0
responsibility, and consequently influences cost
allocation.
• Termination of All Business Activity by the Owner/
Operator: the owner/operator may terminate all
business activity during the operation or following
closure of one or more of its facilities. For example,
the 0/0 may be forced into bankruptcy by large
corrective action costs or by unrelated factors. The
timing of the 0/0's business termination is simulated
based on: (1) average national business termination
rates (to account for causes of business termination not
reflected in the Model); and (2) the relationship
between the costs allocated to the 0/0 and the 0/0's
simulated financial strength. If an 0/0 terminates
business during the facility's operation, then the
facility is forced to cease accepting wastes for
disposal.
The importance of these milestones is that they identify points in time
during a facility's life at which important determinations or decisions are
made. In particular, the timing of these milestones will influence the
allocation of costs among potential funding sources (i.e., PCLTF, state funds,
7 This time period may be adjusted on a case-by-case basis. All
facilities are initially assigned a 30-year post-closure period. However, in
cases where a corrective action is ongoing at the end of the 30-year period,
the period is extended until the corrective action is completed.
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the.Superfund, and the owner/operator). By simulating these key decision
points, the Model analysis can represent the dynamic aspects of facilities'
life cycles.
The attributes simulated at the facility level are displayed in Exhibit
2-2. Also depicted are the values each of the attributes can have and the
bases for the simulation of facility-level estimates. For example, the
facility location is defined in terms of the state, county, groundwater
region, and hazardous waste market region in which the facility is simulated
to be. The simulation of the location is based on the observed distribution
of existing facilities throughout the country.
Some simulated attributes form the basis for simulating other attributes.
For example, facility location and process design are bases for simulating
potential releases. Further, potential releases form the basis (along with
monitoring action) for simulating detected releases. Detected releases are
important for simulating the facility's permit status, PCLTF eligibility, and
actions (monitoring and response) that must be taken. Clearly, the attributes
simulated at the facility level are highly interrelated, and many
interrelationships can be identified from Exhibit 2-2.B
Exhibit 2-2 also provides a sense of the "level of resolution" (i.e.,
degree of detail) of the Model analysis. The level of resolution of a model
is often a controlling factor in its applicability to various issues. For.
example, the status of monitoring and response action is modeled at every
simulated facility. The year the action began, the cost of the action, and
the expected duration of the action are all maintained at the facility level.
Consequently, the simulation of these values can depend on other
facility-level characteristics and can influence other facility-level values.
Therefore, the Model could be used to assess various RGRA action policies
which define those actions that must be taken at facilities based on other
facility characteristics.
The analysis of attributes could have been more detailed or less
detailed.- For example, the status of monitoring and response actions could be
simulated at each disposal process (e.g., surface impoundment) at each
facility. Modeling portions of disposal processes, such as landfill cells,
would be even more specific. On the other hand, an example of less detail
would be performing the analysis on groups of facilities. In choosing the
level of detail1 for various aspects of the PCLTF Simulation Model, the need
for resolution was balanced against the availability of detailed data and the
costs of increased complexity and increased computer requirements. In
general, all important factors influencing PCLTF coverage and costs have been
resolved at the facility level.9
8 The relationships among the facility attributes are discussed below in
section 2.3.2, Relationships Among the Basic Model Units.
9 Appendix A describes in more detail the level of resolution adopted
for each portion of the Model analysis.
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EXHIBIT 2-2
ATTRIBUTES SIMULATED AT THE FACILITY LEVEL
Facility-level
Attribute
Location
Permit status
Status of PCLTF
eligibiity
Disposal processes;
size and design
Financial assurance
mechanism used
Firm which owns/
operates the
facility
Possible
Values of Attribute
State;
County;
Groundwater region;
Hazardous waste market
region.
Status to be determined;
Final RCRA permit received;
Final RCRA permit denied.
Eligibility pending;
Covered by PCLTF;
Disqualified from PCLTF
coverage.
Size in acres;
Prototype design.1
Corporate guarantee or
financial ratios;
Trust fund or insurance;
Surety bond or letter of
credit.
Net worth;
Total assets;
Number of facilities owned
by firm;
Government or privately
owned firm; and
Corporate tax rate for
firm.
Basis for Simulation
Distribution of existing
facility locations. The
distribution is modified
for the purpose of simu-
lating the locations of
new facilities.
Permit policy;
Detected releases.
Eligibility policy;
Detected releases;
Adequacy of financial
assurance mechanism.
Distribution of sizes and
designs of existing
facilities;
Expected distributions for
new facilities.
Distribution of financial
assurance mechanisms used
to date at existing
facilities and used by
firms in different net
worth categories.
Distribution of financial
data on firms owning land
disposal facilities.
1 There are seven prototype designs incorporated into the Model.
Appendix B.
See
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EXHIBIT 2-2 (continued)
ATTRIBUTES SIMULATED AT THE FACILITY LEVEL
Facility-level
Attribute>
Potential releases
and associated dis-
tance to off-site
detection location
Population that: may
potentially become
exposed to hazardous
waste constituents
due to an off-site
release
Detected releases
Status of monitoring
and response actions
Status of claim
Possible
Values of Attribute
Year after facility
opening in which release
is expected to occur;
Distance to off-site
detection location (in
feet).
Number of people.
True or false for each
release type.
Year action began;
Duration of action;
Cost of action.
Cost of claims brought.
Basis for Simulation
Release data;
Facility location;
Process design.
Distribution of ground-
water and surface water
use near disposal facili-
ties;
Number of people drinking
surface water and ground-
water supplied near the
facility location.
Potential releases;
Monitoring actions.
Detected releases;
RCRA policy;
Action data.
Detected releases;
Location;
Population which may
potentially become
exposed;
Claim data.
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2.3.1.3 Release Types Modeled
Seven release types are modeled based on the results of the Release
Simulation Model developed as part of this project. In order to enhance
computing efficiency, the Release Simulation Model was implemented separately.
The same existing facility population which forms the basis for the PCLTF
Simulation Model is used to drive the Release Simulation Model. The Release
Simulation Model produces a series of release matrices which are subsequently
used to simulate releases. The manner in which the release matrices are used
is described in Appendix A. A detailed description of the Release Simulation
Model is provided in Appendix B. This section describes the seven release
types modeled.
A release from a hazardous waste disposal facility can be defined in a
variety of ways. For example, any migration of waste outside of containment
may be considered a release. Alternatively, a release may be achieved only if
a certain critical rate of migration outside of containment is achieved. For
this analysis, six releases are defined according to two characteristics:
location and concentration. A seventh release type is defined in terms of
location only. Release types 1 through 6, displayed in Exhibit 2-3, are
formed by combinations of two locations and four concentrations. Each
location and concentration is described below.
• On-Site Monitoring Well. The on-site monitoring well
is the first location where the owner/operator may find
evidence of a release into groundwater. For facilities
designed with single liners, this location is assumed to
be at the perimeter of the waste, and at the depth of
the water table (i.e., the uppermost aquifer). For
facilities with double-liner leak detection systems,
this location is between the two liners. In discussions
below, this location is referred to as "on-site."
• Closest Off-site Potable Well or Naturally Occurring
Body of Surface Water. The off-site location where
individuals (in the general population) could first
become exposed to the hazardous waste migrating out of
containment into groundwater is the potable well or body
of surface water closest to the facility boundary. This
location represents the point of first potential
off-site exposure. The distance to this location,
referred to here as "off-site," may vary considerably.
Some facilities are within 1000 feet of groundwater
wells used to supply drinking water; others are miles
away from any potable well or surface water.
Distributions describing the distances to this off-site
location that are likely to be observed at land disposal
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EXHIBIT 2-3
SIX RELEASE TYPES DEFINED IN TERMS OF
. LOCATION AND CONCENTRATION
Release Type j
Location
On-site Monitoring Well
. On-site Monitoring Well
On-site Monitoring Well
Closest Off-site Potable Well
or Naturally Occurring Body of
Surface Water
Closest Off-site Potable Well
or Naturally Occurring Body of
Surface Water
Closest Off-site Potable Well
or Naturally Occurring Body of
Surface Water
Concentration
Change in Indicator
Parameters
Detectable Concentration of
Constituents
Toxic Concentration of
Constituents
Detectable through Taste
and Odor
Detectable Concentration of
Constituents
Toxic Concentration of
Constituents
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2-16
facilities were developed based on data from EPA's
surface impoundment assessment survey.10
Change in Indicator Parameters. The concentration
of hazardous waste necessary to result in a change or
increase in an indicator parameter in groundwater has
been defined for each EPA waste code. The indicator
parameters used are total organic carbon (TOC) and total
dissolved solids (TDS).11 The trigger TDS
concentration is defined as 100 percent higher than the
background TDS level.12 The trigger concentration for
TOC is defined as 10 ppm. (The TOC trigger does not
vary throughout the country because most groundwater is
very low in organic content.) This concentration is
referred to here as "indicator parameter" or IP.
Detectable Concentration of Constituents. The
concentration of priority pollutant constituents that
would lead to their identification during routine
analysis is defined as the detectable concentration.
For organic constituents, a 10 g/1 detectable
concentration is used based on the expected sensitivity
of standard gas chromatography/mass spectrometry
analysis techniques.13 For inorganic constituents
analyzed through indirectly-coupled plasma emission
spectrometry, a detection limit of 20 g/1 is used.1*
10 Four separate distributions describing distances to the off-site
location were developed for different parts of the 48 states analyzed in the
Release Simulation Model. These distributions were estimated based on
"Surface Impoundments and Their Effects on Groundwater Quality in the United
States -- A Preliminary Survey," U.S. Environmental Protection Agency,
510/9-78-005, June 1978.
11 The indicator parameters pH and total organic halides were not used
in the Model. pH values were not simulated because there were very little
data about the large influences of local soil conditions. Total organic
halides are reflected in the use of TOC. TDS is used here as a substitute for
conductivity, a commonly used and easily measured indicator parameter. The
conductivities of wastes and naturally occurring bodies of water are not well
catalogued. The TDS for each waste code was estimated using the solubility of
its inorganic constituents. The TOC for each waste code was estimated as the
sum of the carbon portions of each water-soluble organic constituent.
12 Background TDS levels were identified for different areas of the
country from "Surface Impoundments and Their Effects...,'1 op. cit.
13 The analysis techniques are described in "Guidelines Establishing
Test Procedures for the Analysis of Pollutants; Proposed Regulations,"
Federal Register. December 3, 1979, pp. 69464-69575.
l* Ibid.
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2-17-
• Toxic Concentration of Constituents. The toxic
concentration is defined as the criteria level set for
each of the 129 priority pollutants. If the pollutant
is carcinogenic, this concentration implies an increased
risk of cancer of one in one-million based on a 70-year
period of exposure. Toxicity levels for non-priority
pollutants are not defined.
• Detectable through Taste and Odor. Taste and odor
threshold concentrations were identified for the
hazardous waste constituents.16 Taste thresholds were
utilized where available. In cases when only odor
threshold values for airborne concentrations were
available, the concentrations in water needed to yield
the airborne vapor concentration thresholds were
identified.
The seventh release type is different from the six discussed above in that
it does not have concentration as part of its definition. Release type 7 is
defined as the overflow of contaminated leachate from the facility. This
release occurs when the cap on a closed land disposal process allows more
water to infiltrate into the facility than the facility can hold or pass
through. This is often referred to as the "bathtub" effect because it
resembles a bathtub overflowing. It could be caused by the excessive erosion
of a clay cap or the failure of a synthetic membrane cap.
These seven release types were chosen in order to model the process of
detecting and responding to releases. Each release type could be detected by
one or more monitoring actions which may be undertaken at the facility.
Additionally, the detection of each release type may lead to a particular set
of monitoring actions, response actions, or claims.
For example;, routine monitoring actions at the facility may lead to the
discovery of release type 1, IP on site. As the result of this discovery,
more detailed sampling and analysis may be required at the facility for a
certain length of time. This more detailed analysis may be capable of
detecting release type 2, detectable concentration on site, and release type
3, toxic concentration on site, if and where they occur. The discovery of one
or both of these releases may lead to another set of actions, which may
include a corrective action and/or monitoring off site, for example. If
on-site monitoring fails to detect a release, the appearance of taste or odor
at the off-site,location could be the first indication of a release.
The relationships identifying how the detection of releases leads to
monitoring and response actions were designed to reflect current RCRA
regulations. These relationships are discussed more fully in Section 2.3.2,
Relationships Among the Basic Model Units. It is important to note that the
15 F.A. Fazzalori, Compilation of Odor and Taste Threshold Values Data,
American Society for Testing Materials, 1978.
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2-18
seven release types defined above form the lowest level of detail achievable
in the Model's analysis of releases. Actions at the facility level are
simulated as the result of the simulated detection of these releases. This
level of detail enables the Model to be used for a variety of policy
analyses. For example, the implications of alternative RCRA policies which
identify the monitoring and response actions required in response to the
detection of releases can be assessed. The next section describes the actions
and claims simulated in the Model.
2.3.t.4 Actions and Claims
In response to the detection of releases, actions are undertaken and
claims are brought by third parties. This section discusses each in turn,
Actions. Actions have the ability to detect releases that have already
occurred, as well as the ability to prevent future releases from occurring.
Both capital investment and operation and maintenance (O&M) expenditures are
required when an action is undertaken.16 The O&M expenditures (modeled on
an annual basis) last for the duration of the action.
Actions can be divided into three types: monitoring actions, response
actions, and post-closure care. Monitoring actions can only detect releases
that occur. The five possible monitoring actions and the releases which they
can detect are as follows:
* Routine Monitoring for Indicator Parameters at the On-Site
Monitoring Well can detect release type 1, indicator parameters
on site. This monitoring action is analogous to detection
monitoring.17
• Monitoring for Hazardous Waste Constituents at the On-Site
Monitoring Well can detect release type 1, release type 2,
detectable concentration of constituents on site, and release
type 3, toxic concentration of constituents on site. This
monitoring action is analogous to compliance monitoring.18
• Plume Delineation and Tracking On Site is an action that is
not simulated separately in the current runs of the Model;
instead, it is included as a part of fluid removal and treatment,
as described below. The Model does, however, allow the separate
16 Capital expenditures are constrained to be performed no more than
once for each action at each facility.
17 Detection monitoring is required at land disposal facilities. See 40
CFR 264, subpart F, Ground-Water Protection: Detection Monitoring Program.
18 Compliance monitoring is required at land disposal facilities when
detection monitoring indicates a release has occurred. See 40 CFR 264,
subpart F, Ground-Water Protection: Compliance Monitoring Program.
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simulation of plume delineation. When this option is used, plume
delineation and tracking on site can detect release types 1, 2,
and 3. In addition, this detailed tracking program can provide
information identifying whether the plume of concern has already
migrated off site. If the plume has not yet gone off site, this
monitoring action can also identify whether it is likely to go
off site in the future. Plume delineation and tracking on site
is required when an on-site corrective action is performed.
However, it may be defined as a separate action so that
alternative policies defining when this action should be
undertaken can be assessed.19
i
• Monitoring for Hazardous Waste Constituents Off Site can
detect release type 5, detectable concentration of constituents
off site, and release type 6, toxic concentration of constituents
off site. This monitoring action is analogous to on-site
constituent monitoring, but takes place at an off-site location.
It may be undertaken as a result of the detection of an on-site
release or due to the appearance of taste and odor off site
(release type 4).
• Plume Delineation and Tracking Off Site is not modeled as a
separate action in the current runs of the Model; rather, it is
included in off-site, fluid removal and treatment (described
below). As with on-site plume delineation, however, the Model
permits the user to simulate off-site plume delineation as a
separate monitoring action. Plume delineation and tracking off
site can detect release types 5 and 6. In addition, if release
type 6 has not yet occurred, this action can identify whether it
is likely to occur in the future. The likelihood of
concentrations reaching toxic levels (release type 6) can be used
in the Model as a basis for simulating whether certain response
actions should be undertaken.
If any of the five monitoring actions are being performed, then release
type 7, the "bathtub" effect, may be observed (if it occurs) and consequently,
by definition,,, detected.
Each of the monitoring actions described above must be performed for
certain lengths of time. For example, once monitoring for constituents on
site has begun (i.e., compliance monitoring), it must be performed for a
maximum of 30 years or the facility's operating life. The durations and costs
of each monitoring action are described more fully in Appendix A.
19 For example, a policy of requiring plume delineation prior to making
a determination on the need for fluid removal can be modeled. The information
on the potential for off-site migration of the plume may be utilized in the
decision regarding the need for fluid removal.
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Three response actions are performed to clean up releases that have
occurred and to prevent future releases. Each action has a required
duration. If the full duration is performed, then the response action is
fully effective in cleaning up and preventing its relevant releases. If,
however, a response action is stopped prematurely (e.g., due to a lack of
funding), then the action may be less than fully effective.28
The three response actions and the releases they influence are as follows:
• Cap Repair will prevent the occurrence of release type 7.
• Fluid Removal and Treatment On Site will prevent the
occurrence of release types 1 through 6 if they have not already
occurred. Additionally, this response action cleans up the
groundwater contaminated through release types 1, 2 and 3. In
the current runs of the Model, this action includes necessary
plume delineation actions and is analogous to a corrective
action. Because source removal is not currently required by
regulation, the duration of this action is essentially infinite.
Were it to be stopped, plume growth would immediately resume.21
• Fluid Removal and Treatment Off Site will prevent the
occurrence of release types 4, 5 and 6 if they have not yet
occurred. This response action includes plume delineation and
cleans up the contaminated groundwater off site. As with fluid
removal and treatment on site, its duration is essentially
infinite.
Post-Closure Care refers to those routine activities undertaken after
the facility ceases accepting waste for land disposal and is closed. The
major activities comprising post-closure care include:
• site security, including maintaining a fence;
• maintaining the cap; and
• leachate collection and treatment.
The costs and performance of these routine activities are not influenced in
the Model by the initiation of other actions.
20 The effectiveness of partially-completed response actions is modeled
probabilistically. If a release is not fully prevented, it may also be
delayed. A set of physical relationships is used to model the effectiveness
of releases.
21 Corrective action is required at land disposal facilities when
compliance monitoring demonstrates certain levels of groundwater
contamination. See 40 CFR 264 subpart F, Ground-Water Protection:
Corrective Action Program.
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Claims. Claims are brought by third parties who believe they have been
harmed by an off-site release. The four claim types are:
• Personal Injury. Medical costs, lost time due to disability,
lost time due to premature death, and medical monitoring costs
comprise personal injury claims.
• Real Property. Real property claims reflect a reduction in
the value of housing and farmland near the location of the
off-site release.22
• Economic Loss. The capital cost of replacing a contaminated
source of drinking water is used to approximate economic loss
claims.23 This claim is only calculated where the contaminated
source is simulated to be used for drinking.
• Natural Resource Damage Claims. The cost of restoring small
areas of contaminated surface water, the value of lost recreation
for one year, and the commercial value of potential fish kills
comprise the estimate of natural resource damage.
These claim types are considerably broader and more inclusive than the claims
currently covered by the Superfund. This broader coverage is due to the fact
that the PCLTF covers liabilities under CERCLA and any. other state or federal
law.
Of particular note is that the simulation of personal injury claims is
not based on an estimate of the number of people expected to suffer from
adverse health effects as the result of exposure to hazardous waste
constituents. The incremental increase in the incidence of certain diseases
due to exposures to the types of releases simulated here is not expected to
form the majority of potential personal injury claims. Instead, much larger
costs will come from two different groups:
• individuals who suffer from conditions that they would
have had even if the release had not occurred, but for
which a causal link to the release cannot be proved or
disproved; and
• individuals who do not exhibit signs of adverse health
effects, but who feel themselves to be at an increased
risk.
22 Property values at the county level were obtained from U.S. Bureau of
the Census, County and City Data Book, 1977; these data are adjusted to 1982
based on changes in the Consumer Price Index between 1977 and 1982.
23 The costs of alternative water supplies were obtained from U.S.
Environmental Protection Agency, Technologies and Costs for the Removal of
Fluoride from Potable Water Supplies, Final Draft, July 1, 1983, pp. 58-78.
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The first group is referred to as those with the "background incidence" of
the types of diseases and conditions believed to be related to exposures to
toxic substances. Because the individuals with the background incidence
cannot be distinguished from those with the incremental incidence (i.e., those
whose condition was caused by the release), the individuals with the
background incidence are potential claimants. Because the background
incidence is much larger than the incremental incidence, the magnitude of the
medical cost and lost time components of personal injury claims are modeled
using the background rates of incidence. In this manner, the need to estimate
incremental incidence is avoided.
The group of individuals who believe themselves to be at increased risk
may claim for the costs of medical monitoring (i.e., routine checkups to test
for the. emergence of diseases or conditions potentially caused by their
previous exposure). Because this group of individuals can be very large, the
medical monitoring cost component is a significant portion of the simulated
personal injury claims. Medical monitoring costs are estimated at $160 per
person per year (1982 dollars).
It should also be noted that the site-specific nature of the likely extent
of natural resource damage, and the lack of detailed data on key site-specific
variables prevented the full quantification of these potential claims. Of the
four claim types modeled, natural resource damage claims are the most
incomplete in their characterization.
2.3.1.5 Funding Sources
The costs of actions and claims must be allocated to one of four potential
funding sources: PCLTF, state funds, Superfund, and owners/operators. In the
Model, if no funding source is available for an action, then the action is
not taken. This point is particularly important because if, for example,
monitoring actions are not undertaken, off-site releases may occur which
otherwise would have been prevented. If no funding source is available for a
claim, then the claimant is not compensated.
In order for a cost to be allocated to the PCLTF, the following two
conditions must be met:2*
• the facility at which the cost arises must be qualified
for PCLTF coverage; and
• the cost must be of a type which the Fund covers.
The ability of state funds to cover costs is simulated using a series of
representative state coverage regimes. Each state is probabilistically
assigned one of the regimes which defines whether the state has a fund and the
types of costs the state fund will cover. If state-covered costs arise at .
facilities within the state, the costs may be allocated to the state fund.
2* How these two conditions are modeled is described below in the
section on Allocation Policies.
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States are also simulated to cover a portion of Superfund-initiated actions
(10 percent of, costs at privately-owned facilities, 50 percent of costs at
state and municipally owned facilities).
The owner/operator of a facility may also cover costs that arise. For the
owner/operator to cover costs, one of the following two conditions must be met:
» the owner/operator has an active financial assurance
mechanism (such as insurance or a trust fund) dedicated
to covering the type of cost being allocated; and
• the owner/operator is still in business (even if the
facility is closed) and consequently pays the cost of
the action or claim.
It should be noted that financial assurance is currently required only for
•routine monitoring and care during the post-closure period. Consequently,
most costs (e.g., corrective action costs) would not be covered by a financial
assurance mechanism.25
Also of note is that the allocation of costs to owner/operator can force
them into bankruptcy. If the cost of an action is initially allocated to an
owner/operator who subsequently terminates business, the cost is reallocated
to one of the other funding sources. If neither of the other funding sources
covers the.cost of the action, then the action is stopped.
Although hazardous waste generators may be potentially liable for costs
arising at land disposal facilities, they are not included in the model as a
funding source at this time. The importance of generators as a funding source
depends, in part, on the interpretation of the transfer of liability to the
PCLTF and the policies of cost recovery undertaken by EPA. Insofar as EPA is
able to recover PCLTF expenditures from generators, the PCLTF balances may be
underestimated by the Model.
2.3.2 Relationships Among the Basic Model Units
The relationships among the Basic Model Units described in the previous
section can be divided into the six parts displayed in Exhibit 2-1 (see page
2-4). Each set of relationships is described below.
2.3.2.1 Economic Relationships
Several Model Units are linked together by the laws of supply and demand.
These links are expressed within a series of economic relationships which
defines how simulated quantities influence one another. Proper identification
25 Section, 108(b) of CERCLA authorizes the President to promulgate
requirements that facilities establish and maintain evidence of financial
responsibility'consistent with the risks they pose. Alternative assumptions
regarding these requirements can be modeled to help identify their importance
and usefulness as a funding source.
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and specification of these various links are necessary for the Model's
internal consistency.
The most important economic relationship in the Model is between the
demand for land disposal and the simulated facility population. The demand
for land disposal is divided into four regional markets. The Model ensures
that there is sufficient disposal capacity in each market region to meet
demand by adding new disposal capacity as existing facilities close. New
disposal capacity is created by adding new facilities to the facility
population.
The demand for land disposal is driven by the rate of growth of industrial
output, and by changes in the real prices of land disposal and substitutes for
land disposal (e.g., incineration). If the real prices of land disposal and
its substitutes remain fixed, then the demand for waste disposal would be
expected to grow at the same rate as industrial output growth. As the real
prices change, demand for waste disposal will change, and this change will
influence the facility population.
Currently, there is considerable uncertainty regarding the future prices
for land disposal and its substitutes; consequently, the future demand for
land disposal is also very uncertain. The Model is structured to allow the
use of a wide variety of assumptions in this regard.
Despite the overall uncertainty regarding the future of land disposal, it
is clear that future funding mechanisms for the PCLTF could influence the
price of land disposed. If, for example, the PCLTF tax rate were increased
substantially, it could reduce the future demand for land disposal by
increasing its real price. In order for the Model to produce internally
consistent results, this feedback between a PCLTF tax increase and the demand
for land disposal must be incorporated. The links specified between price,
demand, and the facility population enable the Model to simulate this
important feedback.
2.3.2.2 RCRA Policies
RCRA policies are an important driving factor in the PCLTF Simulation
Model. Most significantly, they influence the facility-level characterization
discussed above. The two RCRA policies of particular importance in the
analysis of the PCLTF are: (1) the process by which permits will be granted
(or denied) at interim status facilities; and (2) the specification of those
actions that must be undertaken at facilities in response to the detection of
releases.
The Model has been developed to allow a wide variety of policy assumptions
to be used. However, for the analyses presented in this report, a set of
policies has been assembled which, in the opinion of EPA, is believed to
reflect current Agency policy. The following discussion presents how the
policies are defined for modeling purposes and how they form links among the
Basic Model Units.
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Permitting Policy. The permitting policy links the types of releases
simulated to occur with the determination of whether (and when) an interim
status facility receives a final RCRA permit.26 This link is particularly
important for assessing PCLTF adequacy because a final RCRA permit is a
condition for Fund coverage. The permitting process may weed out high-risk
facilities, thereby reducing Fund outlays. Consequently, the modeling of the
permitting process must incorporate its potentially-selective nature.
The permitting policy is defined by four characteristics:
• the rate at which permit determinations (granted or •
denied) will be made;
• the "evidence" that must be presented by the facility;
• the decision rule used to make the permit decision; and
• the fraction of owner/operators who close their
facility rather than get a permit.
The permitting1 rate is defined in terms of the number of years it will take
EPA to request' and evaluate permit applications from the population of interim
status land disposal facilities. If it takes a long time for EPA to perform
this task, facilities may be simulated to close prior to a permitting
determination being made. Of note is that although EPA is attempting to
request final permit application first from "problem" facilities, this aspect
of the permitting process is not modeled.
The relevant evidence that must be presented by the facility in its permit
application is;information on the existence and extent of groundwater
contamination at the facility. This information is modeled in terms of
whether any of'the seven release types has occurred and has been detected at
the facility. The occurrence of the release types is simulated at the
facility level as described above in the' facility-level characterization.27
Whether the release types are detected depends on the extent of site
investigation required in the permit process.
At a minimum, the results of the facility's detection monitoring program
(capable of detecting release type 1, change in indicator parameters on site)
will be utilized in a facility's permit review. Consequently, the evidence
supplied by this monitoring activity is modeled to be part of the permit
decision process. In addition, the Agency could require a more extensive site
assessment. Although not utilized in the simulations presented in this
26 All simulated new facilities are presumed to -open with a final RCRA
permit. Consequently, the permitting policy does not affect new facilities.
27 Site-specific data on the existence of groundwater contamination are
not available, and are therefore not utilized in the Model.
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report, the ability to model a more detailed site assessment process has been
developed.
The permitting decision rule defines whether, based on the evidence
presented, the facility is granted or denied a final RCRA permit. The rule
can specify those releases (including combinations of releases) that result in
a permit being denied. (In the simulations presented in this report, the
permitting rule specifies that releases do not prohibit a facility from
obtaining a final RCRA permit.) Of note are those factors not included in the
Model's permitting decision rule, including: past or current violations of
RCRA regulations, inability to demonstrate financial assurance, and
waste-liner compatibility. Insofar as these and other factors play an
important role in the denial of RCRA permits, the Model may overestimate the
extent to which interim status facilities obtain permits, and consequently,
underestimate the need for new land disposal capacity.
Instead of applying for a permit, some owners/operators will prefer to
close their facility. For example, an owner/operator may decide to transport
waste to another facility and close his facility instead of applying for a
permit. This process is modeled by allowing a user-supplied fraction of
existing facilities to close rather than obtain final permits. (Current
simulations set this fraction at 25 percent.)
Action Policy. An action policy is constructed in the Model to
identify those actions that must be undertaken in response to the detection of
releases. The Model uses a set of action rules which reflects the current EPA
policies regarding detection monitoring, compliance monitoring, corrective
action, and post-closure care. However, the action rules are completely
general. They can be used to identify when any of the possible monitoring and
response actions are started (and stopped) as a function of when releases are
detected.
2.3.2.3 Financial Relationships
Financial relationships are used to assess the viability of firms.
Facilities are owned by firms which may incur costs for monitoring, corrective
action, and claims at one or more of their land disposal facilities. These
costs may force the firm to go bankrupt. Bankruptcy leads to the closing of
all the firm's operating facilities and the elimination of the firm as a
potential funding source. If an alternative funding source is not available
to cover the costs for ongoing actions, then the actions are terminated. .
Consequently, the financial relationships are an important driving force in
the Model because they determine the rate at which firms are not able to cover
expenditures.
The viability of each firm is assessed annually. The likelihood of a firm
going bankrupt depends on the monitoring, response, and claims expenditures
currently being incurred by the firm and on the financial status of the firm.
The likelihood is evaluated annually because there is an annual chance of
bankruptcy due to the background failure rate and because the expenditures
incurred by the firm may change each year due to the initiation or termination
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of actions at one or more of its facilities. All the factors that influence
the likelihood of a firm going bankrupt are presented in Exhibit 2-4.
ii
Given the firm's financial characteristics and its annual expenditures, a
financial relationship is used to determine the likelihood that a firm will go
bankrupt in the current model year.28 Whether the firm goes bankrupt is
simulated based on the background bankruptcy rate associated with the firm's
net worth and the likelihood of expected net income exceeding the increases in
costs associated with monitoring, response, and claims.
2.3.2.4 Allocation Policies
The Model's general framework allows the specification of a wide variety
of allocation policies. The allocation policies identify the manner in which
costs generated at the facility level are allocated to the possible funding
sources (PGLTF, state funds, Superfund, and owner/operators). The policies
are identified by a series of allocation rules which identify how each cost
arising at the facility level is to be allocated in various situations. In
addition, there are a set of rules which define how facilities may qualify for
PCLTF coverage. The Fund qualification rules are a key component in cost
allocation. Each of these rules is discussed below.
Allocation Rules. In performing the cost allocation, the allocation
rules specify which of the funding sources may be used and the order in which
they are used. , The order is particularly important because if the funding
source specified as first (e.g., the owner/operator) is able to pay the entire
cost, then the second and third funding sources (e.g., PCLTF and state funds)
would not be called on to cover the cost.
The rules are constructed so that the allocation of costs will depend on
the following facility-level characteristics:
• where the facility is in its life cycle (e.g.,
operating, closed for less than 30 years, closed for
more than 30 years);
• whether the facility is an existing or new facility;
• whether the facility has received a final RCRA permit;
and;
• whether the facility has qualified for PCLTF coverage
(the facility's PCLTF eligibility may be pending,
qualified, or unqualified).
28 See Appendix A for a detailed description of the procedure used to
assess a firm's; viability.
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EXHIBIT 2-4
FACTORS INFLUENCING THE LIKELIHOOD
OF FIRM BANKRUPTCY
Firm Characteristic
Number of facilities owned by
firm.
Firm's net worth.
Total capital, O&M, and claims
expenditures; and Federal
corporate tax rate.
Firm's total assets.
Firm's expected after tax net
income and after tax expenditures
for ongoing actions and claims.
Role in Evaluation of Firm's Viability
Estimation of monitoring, response, and
claims expenditures incurred by firm at
its facilities.
Definition of the background rate of
bankruptcy.
Estimation of firm's expected after
tax expenditures for ongoing actions
and claims at its facilities.
Estimation of firm's expected after tax
net income.
Likelihood of bankruptcy failure
associated with firm.
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For example, a closed facility which has qualified for PCLTF coverage will
have its costs allocated differently than a facility which is still in
operation.
These rules are also the means by which the Model user identifies the
costs that will be covered by the PCTLF. Because the allocation rules can
differentiate among each of the different cost types, selected costs may be
allocated to the PCLTF in various situations. For example, post-closure care
costs at qualified facilities may be defined to be covered by the Fund only
after the end of the post-closure period. In addition, because the rules
define the order in which allocation takes place, the Fund coverage can be
modeled as coming either before or after recovery from owners/operators, state
funds, and Superfund.
Fund Qualification. One important facility characteristic driving the
allocation of costs is whether the facility owner/operator has transferred his
or her liability to the PCLTF. When this transfer of facility liability takes
place, the facility is said to be qualified for Fund coverage, or "qualified."
A facility which is denied Fund coverage is called "unqualified." Before a
determination is made regarding a facility's ability to qualify for Fund
coverage, the facility is said to have its qualification status pending.
A series of qualification rules is used to determine the qualification
status of each facility over time. Both the timing28 of the qualification
determination and the bases for disqualifying a facility can be varied.
The facility-level characteristics that can influence the qualification
determination are:
• whether releases have been detected;
• whether all necessary actions have been taken in
response to the detected releases; and
• whether the owner/operators' required financial
assurance has been adequate through the time of the
determination.
Based on the simulated values of these facility-level characteristics, the
facility may become qualified or be disqualified.
This structure for the qualification rule is somewhat limited because it
does not incorporate other potential causes of facility disqualification, such
as past violations of RCRA regulations. However, for the purposes of
assessing PCLTF adequacy, it selectively weeds out high-risk facilities based
on their past performance.
29 CERCLA defines the timing as not to exceed five years following
closure.
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2.3.2.5 Legal Validity of Claims
Legal issues arise when third parties make claims against owners/operators
or the PCLTF. In order for a third-party claim to be legally valid, it must
be recoverable under an applicable theory of law or state statute. Statutes
and theories of law vary from state to state throughout the country, meaning
that the legal validity of a claim will depend upon the statutes and
established legal theories of the state in which the claim arises.
To model this phenomenon, a set of seven representative legal regimes was
developed based on a review of existing state statutes and federal and state
common law theories. These legal regimes form a link between claims simulated
at the facility level and the allocation of these claims to the potential
funding sources. Each legal regime defines the likelihoods that personal
injury claims, real property claims, and economic loss claims have for being
legally valid. Natural resource claims are always assumed to be legally valid
because they are specifically identified in CERCLA as a liability of the
owner/operator.
The seven legal regimes are probabilistically assigned to the states. As
claims arise at facilities, they are evaluated under the legal regime assigned
to the state in which they are located. The likelihoods of the claims being
legally valid differ not only across the legal regimes, but also by claim type
and release type.
Of note is that legal regimes do not evolve over time within the Model.
An assessment of the legal regimes that exist today is used throughout the
time horizon of the analysis. Because recent trends in the evolution of state
statutes and legal theories appear to be in the direction of increasing the
bases of recovery for the types of claims modeled here, the simulated legal
validity of claims may be an underestimate of their future legal validity.
2.3.2.6 The Effect of Response Actions on Releases
(Physical Relationships)
The final set of Model relationships describes physical phenomena at the
facility level. These relationships primarily identify the influence that
undertaking' response actions has on the contaminated groundwater at the
facility. For modeling purposes, this important relationship is defined as a
feedback from response actions to releases.
As described above in the section on actions and claims, each response
action has associated with it a set of releases which it is capable of
cleaning up or preventing. If a response action is performed for its full
duration, it is assumed to be fully successful in its cleanup and prevention
of releases. However, a method was required to evaluate the effectiveness of
response actions that are only partially completed.
The preferred-approach for estimating this relationship would be to model
the physical processes inherent in the response actions at the facility
level. However, a simpler parametric approach was adopted. The effectiveness
of response actions is assumed to be a function of the portion of the action
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completed. The greater the portion completed, the more likely the action is
to be successful.
An improved understanding of response actions and their effects on current
and future groundwater contamination is clearly desirable. For the purposes
of this Model, however, the results are not sensitive to a wide range of
relationships examined. This lack of sensitivity is due primarily to the fact
that most response actions are simulated to be performed for their full
duration. However, under alternative cost allocation policies, greater
numbers of response actions could be left incomplete. In such situations,
assumptions regarding these relationships could have more impact on the
Model's results.
This chapter has presented a non-technical overview of the PCLTF Model.
Greater detail on the topics covered is found in Appendices A and B in Volume
III of this report. The following chapter discusses how the Model is used to
assess PCLTF adequacy.
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CHAPTER 3
HOW THE MODEL IS USED TO ASSESS PCLTF ADEQUACY
The PCLTF Simulation Model described in the previous chapter is a tool for
analyzing RCRA"-regulated land disposal facilities and their relation to the
PCLTF. This chapter presents the concepts underlying the use and
interpretation of the Model, and discusses the concept of PCLTF adequacy.
3.1 USE OF THE PCLTF SIMULATION MODEL
The PCLTF Simulation Model, like all models, is a simplified representation
of a complex situation. The Model is constructed so that it represents, to
the greatest extent possible, behavior in the real world. However, because
the Model is a" simplification, special care must be exercised in using it.
To use the< Model to assess the future performance of the PCLTF, the
following steps are performed:
• develop a set of assumptions and data describing the
most likely future configuration of the PCLTF (often
referred to as a "Base Case");
* investigate the sensitivity of the Model's results to
the assumptions and data employed; and
• perform simulations of potential alternative
configurations of the PCLTF.
The Base Case provides the best estimate of the likely future performance of
the PCLTF. Consequently, careful consideration must be given to the choice of
inputs used to define it. The Base Case also supplies a standard against
which all subsequent Model runs can be compared. In this report, Simulation 1
presented in Chapter 4 can be thought of as a Base Case.
Once a Base Case is defined, the sensitivity of the results to the
assumptions and data employed must be examined. Because the Model simulates a
simplified world, it is important to identify the effects that the
simplifications have on the results.
The sensitivity of the results to most factors can often be seen very
readily. For example, if the costs of post-closure care are a very small
portion of total PCLTF expenditures, then the estimates of the PCLTF balance
over time would not be sensitive to data describing the magnitude of these
costs. For those factors whose relationship to the Fund balance are more
difficult to identify (e.g., the specification of the ability of
owners/operators to cover costs without going bankrupt), model runs can be
performed with alternative inputs. These analyses would be called sensitivity
runs of the Model.
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Finally, various Fund configurations can be assessed using alternative
policy assumptions. These policy simulation runs differ from the Base Case by
the way in which the Fund is assumed to be implemented. Unlike the
sensitivity runs, the policy simulations are not primarily designed to assess
how the Base Case results are affected by various assumptions. Instead, they
are used to assess how alternative Fund configurations would perform over time.
The Model's ability to perform policy simulations makes it a valuable tool
for enlightening the debate surrounding PCLTF implementation. Using the
Model, the implications of a wide variety of Fund structures can be examined.
The policy simulations may vary along a variety of dimensions, including:
PCLTF qualification requirements, the types of costs covered by the PCLTF, and
PCLTF funding. Such policy simulations are presented in Chapter 4.
Policy simulations can also be performed to analyze a variety of other
questions. Some questions may be closely related to the PCLTF, such as
identifying the likely magnitude of costs expected to arise at facilties not
covered by the PCLTF. The Model provides estimates of all the non-PCLTF costs
arising at land disposal facilities over time. Some of these costs, for
example, may ultimately become the responsibility of the Hazardous Substance
Response Fund (Superfund). The potential distribution of future costs between
the PCLTF and the Superfund can be assessed.
Numerous other policies can be examined to various degrees with the
Model. As described in Chapter 2, the Model incorporates most of the major
factors influencing land disposal facilities. Therefore, the Model is a
particularly valuable tool for assessing the overall implications of various
policies on this population.
Particular care must be exercised, however, in the application of the
Model to uses for which it is not intended. For example, the data that drive
the Model are not sufficiently detailed to allow, site-specific estimates of
facility performance to be made. Instead, the Model is designed to evaluate a
population of facilities. (If site-specific data were available, the Model
could be used on a site-specific basis.)
Similarly, as mentioned above, the Model is a simplified representation of
the complex world. In developing the Model, certain factors which are not
important for the assessment of the PCLTF have been omitted. However, because
these omitted factors may be very important for alternative analyses, a
careful review of the Model's capabilities and assumptions is warranted before
undertaking analyses which are clearly unrelated to the PCLTF. Chapter 2,
Appendix A, and Appendix B of this report may form the basis for such a review.
3.2 INTERPRETATION OF THE MODEL RESULTS
i
The PCLTF Simulation Model is a stochastic (i.e., probability-based)
model. This means that the results it produces are probability distributions.
The ability to produce distributions differentiates the Model from other
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3-3
models that produce single values or point estimates.1 This ability helps
to make the Model a powerful tool, but demands an increased level of
sophistication on the part of the user to interpret the results.
Before describing how the Model results are interpreted, several terms
must be defined. A single run of the Model produces a set of results. The
results for a run are driven by the inputs used to specify the run, and by the
data and relationships built into the Model. Runs differ from one another in
terms of the inputs used. The underlying Model data and relationships remain
unchanged.
A single run is made up of numerous iterations, or sequential operations
of the Model under the same set of inputs. During each iteration, the Model
performs all the calculations necessary to estimate the PCLTF balance over
time. The results from one iteration to the next will vary because they
depend on events which occur probabilistically. The occurrence of the
probabilistic events is modeled using random numbers.2 These probabilistic
events are the sole basis for differences among iterations within a single run.
The results of each iteration in a Model run are statistically independent
and equally likely. Each iteration is analogous to a strictly controlled
experiment, and the results of the numerous iterations can be combined into an
estimate of the d istribut ion of the PCLTF balances over time. Although the
outcomes of the controlled experiments may vary, the variance may be
attributed to random factors. .
For example, a given Model run may be made up of 29 iterations. This
means that for"each of 100 years, the Model will estimate the PCLTF balance
(and everything else as well) a total of 29 times; for example, there will be
29 estimates of the balance in year 25 (and in every other year of the run).
The 29 estimates of the balance in year 25 can be described in terms of their
distribution. The distribution can be graphed, and its nature summarized by
various statistics.
The statistics used to summarize the estimated distributions provide the
basis for interpreting the Model results. Consequently, the choice of
statistics is important. Fortunately, standard statistical measures are
available which identify the two most important aspects of the distributions:
(1) their central tendency; and (2) their spread. To interpret the Model
results,, one oust be familiar with the statistics used.
The central tendency of a distribution represents, as the name implies,
its "middle" value. Two statistics describing the central tendency are used
in the Model, the mean and the median.
1 Models that produce point estimates are often referred to.as
deterministic models.
2 Appendix C describes the random number generator used.
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The mean is the average value observed across the 29 iterations. As such,
it is an estimate of the expected value of the distribution. For example, the
mean PCLTF balance is reported for each year in a Model run. In interpreting
the mean, one roust realize that the reported quantity is itelf an estimate of
the underlying true mean of the distribution. Consequently, there is
uncertainty surrounding the estimate of the mean. This uncertainty is
expressed as the standard error of the mean. The standard error indicates how
the spread of the distribution influences the estimate of the mean.
The median of a distribution is also an indication of its central
tendency. The median is the value for which there is a 50 percent chance of
an observation falling either above or below it. The median is considered to
be more "robust" than the mean because its value is not influenced by extreme
values in the distribution.
The spread .of the distributions is identified using the tenth and
ninetieth percentiles of the observations. These quantities estimate the
values below which 10 percent and 90 percent of the observations would be
expected to fall, respectively. For example, if the estimated tenth
percentile for the Fund balance in year 30 is $300 million, this means that
given the uncertainties reflected in the Model, there is a 10 percent chance
of the Fund being below this amount in this year.
The greater the number of iterations in a given Model run, the greater the
confidence that the distributions generated and summarized as Model output
resemble the actual distributions which would result from the Model
assumptions and parameters. In other words, the larger the number of
independent observations of the characteristics of a phenomenon, the better
the characterization of the phenomenon, given the accuracy of the techniques
used for measuring the attributes of the characteristics. Estimates can be
improved by undertaking additional independent observations, but the accuracy
of the estimates can never exceed the underlying accuracy of the measurement
techniques. This analogy is consistent with the design philosophy
incorporated in the Model. There is value in performing numerous iterations,
but only up to a point. Of course, added iterations add to computer costs for
running the Model.3
In interpreting the Model results, one must carefully consider which, if
any, of the statistics described above are appropriate for addressing the
question(s) at hand. For some applications, the characterization of the
entire distribution may be important. Consequently, all the statistics
discussed above may be relevant. For others, an estimate of a likely "worst
case" may be all that is required, making the tenth percentile estimate the
most important. Several observations regarding those statistics most relevant
for assessing PCLTF adequacy are presented next.
3 Appendix C describes how the number of iterations performed influences
the characterization of the Model results.
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3.3 ASSESSING PCLTF ADEQUACY
Drawing conclusions regarding the adequacy of the PCLTF clearly requires
subjective judgments regarding "how adequate is adequate." This section
briefly presents how the Model results aid in the process of making these
subjective judgments.
The question of adequacy begins with the identification of objectives for
the Fund's performance. The main objective may be, for example, that the Fund
be likely to have sufficient revenues to cover expenditures over the
foreseeable future. Although this objective may appear straightforward, the
use of the word "likely" opens up the objective to a variety of
interpretations regarding "how likely." Different interpretations of "likely"
require the use of different output statistics; for example:
• Very likely. If future revenues must be very likely to be
sufficient to cover future expenditures, then the tenth
percentile estimate of the Fund balance may be most relevant for
assessing adequacy. In this case, there is only a 10 percent
chance that the balance will fall below the tenth percentile. A
less conservative percentile "safety line" would be the
twenty-fifth percentile.
• More likely than not. If future revenues must be more
likely than not to be sufficient to cover future expenditures,
than the median estimate of the Fund balance may be most relevant.
In general, the Fund balance statistics one chooses to use will depend on
one's inherent conservatism "and one's sense of the robustness of the Model
output.
There may, of course, be a variety of objectives for Fund performance.
For example, one may not want the Fund to become extremely large. Also, it
may be desirable to have a Fund which is stable, meaning a Fund which is not
sensitive to expenditure shocks. Objectives may be related to factors other
than Fund balance, such as the extent to which funds are available to
undertake corrective actions at land disposal facilities.
The likelihood of the Fund attaining its various objectives can be
assessed using the Model. In addition, as described above, various policy
simulations can be performed to identify the Fund configurations that are roost
likely to attain the stated Fund performance objectives.
In using the Model to draw conclusions regarding PCLTF adequacy, one must
always keep in mind that the Model is only a tool, presenting a simplified
analysis of a very complex world. As such, the Model results are not answers,
but merely indications of how the world is expected to behave under a given
set of assumptions. Their value lies not only in the estimates of the PCLTF
balance, but also in the insight they provide into the relative importance of
the factors influencing Fund performance. The next chapter presents the
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results and important insights gained from the five simulations. Chapter 5
presents further analyses of Fund revenues under a variety of Fund management
policies.
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CHAPTER 4
SIMULATION RESULTS
This chapter first presents a detailed description of Simulation 1, which
represents EPA's current view of the most likely set of policies to be
embodied in the Fund. The policies and assumptions used are described, and
the Model results are presented. Then, each of four additional policy
simulation runs defined by EPA is presented separately. Each run is defined
in terms of how it differs from Simulation 1, and emphasis is placed on how
the results differ from the Simulation 1 results. Following the presentation
of the simulation descriptions, brief sections summarize the Model results and
compare the distribution of costs among potential funding sources. Finally, a
conclusions section is presented.
4.1 SIMULATION I
4.1.1 Definition of Simulation I
<:
For the purpose of defining this simulation, a set of inputs has been
assembled which EPA believes best reflects the current PCLTF statute.
(Appendix D displays all the inputs used in Simulation 1.) Clearly, numerous
EPA policies regarding the implementation of the Fund must be anticipated.
The assumptions used here do not necessarily reflect future Agency policies.
Instead, this analysis and the Model upon which it is based form a tool which
the Agency and Congress may find useful in their assessments of alternative
PCLTF policies'and legislation.
The following major assumptions are incorporated into this and all the
other policy simulations:
• Initial Facility Population. The initial facility population
incorporates all existing interim-status land disposal facilities
with the following processes: surface impoundment (storage,
treatment, and disposal); landfill; land treatment; and injection
well. The size of the initial facility population is estimated
using the most recent data on land-disposal facilities compiled
by EPA; it is approximately 980 facilities nationwide.
• Addition of New Facilities to the Facility Population. New
facilities are added to the facility population as additional
land disposal capacity is required to meet the demand for land
disposal. Additional capacity is required over time because
existing facilities are simulated to close. The demand for land
disposal is assumed to decrease by two percent per year for the
next 50 years (beginning October 1, 1983), and then to remain
constant (Simulation 2 varies this assumption).
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• Facility Characterization. Existing facilities in the
facility population are characterized according to the most
recent population data, obtained by EPA. New facilities are
simulated to conform with the new land disposal regulations
promulgated by EPA.* New facilities are assumed to have better
locations than existing facilities (in terms of the likelihood of
releases and potential exposure to contaminated groundwater).
• RCRA Policy Regarding Facility Permits. Final RCRA permits
are assumed to be granted or denied to all existing facilities
within the next five years. Any facility may be granted a
permit, regardless of the evidence of releases that may have
occurred. However, it is assumed that 25 percent of existing
facilities will close rather than obtain final permits. This 25
percent is chosen randomly in each iteration. No other
permitting requirements are used in this simulation.
• RCRA Policies Regarding Monitoring and Response Actions.
All owner/operators are simulated to undertake detection
monitoring and, when necessary, compliance monitoring as required
by current land disposal regulations. In addition, monitoring
outside the facility boundaries is simulated in the event that
on-site monitoring reveals that a release has migrated off site
(however, this monitoring is not paid for by owner/operators).
Corrective action (both on site and, when necessary, off site) is
simulated when the results of compliance monitoring indicate
groundwater contamination (the owner/operator only pays for
on-site cleanup).
• Third-Party Claims. Third-party claims are simulated in
response to off-site releases only. The legal validity of
third-party claims is simulated to reflect current state and
federal statutory and common law.
A set of policies defining the manner in which the PCLTF will be
implemented is also required. The PCLTF implementation policies are listed in
Exhibit 4-1 and discussed below. These policies differ in the policy
simulation runs. In Simulation 5, the PCLTF is omitted; thus, these policies
are not applicable.
Qualification for PCLTF Coverage. In order for a land disposal
facility to qualify for Fund coverage (i.e., transfer its liabilities to the
Fund as outlined in CERCLA section 107(k)), it must meet the conditions listed
in Exhibit 4-1. The final RCRA permit requirement and the five-year period of
monitoring are CERCLA requirements. The demonstration of "no substantial
likelihood" of migration off site, release, or risk to public health required
by CERCLA is interpreted to mean that no release be detected prior to the end
1 40 CFR 264.
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EXHIBIT 4-1
PCLTF IMPLEMENTATION POLICIES1
• .Qualification for PCLTF Coverage
-- Final RCRA permit received.
.-- Qualification monitoring period of five years.
No releases detected prior to the end of the five-year
qualification monitoring period.
Financial assurance for post-closure care remains active
throughout the five-year qualification monitoring period.
• Fund Coverage at Qualified Facilities
All third-party claims.
Non-routine monitoring and response actions following the 30-year
post-closure period.
Routine monitoring and care following the 30-year post-closure
period.
• Fund Revenues
-- Tax of $2.13/dry-weight ton fixed in nominal dollars.
-- Fund ceiling of $200 million fixed in nominal dollars.
Fund earns 5.1 percent nominal interest on any positive Fund
balance.
1 Not applicable to Simulation 5 which does not include the PCLTF.
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of the five-year qualification monitoring period. This means that any release
detected prior to this time (including during operation) would disqualify a
facility from Fund coverage. Finally, the financial assurance requirement
listed in Exhibit 4-1 is included to reflect the CERCLA provision requiring
that the facility comply with all RCRA regulations which may affect the
performance of the facility after closure (CERCLA section 107(k)(1)(A)).
Fund Coverage at Qualified Facilities. The PCLTF is authorized to
cover the costs of monitoring, response, and claims established in CERCLA or
any other state or federal law (CERCLA section lll(j)). Fund coverage.
includes all third-party claims immediately following the transfer of
liability to the Fund. Based on EPA's interpretation of CERCLA, non-routine
monitoring and response actions, and routine monitoring and care (including
detection monitoring, compliance monitoring, and post-closure care such as
leachate collection and security) are assumed to remain the responsibility of
the owner/operator throughout the 30-year post-closure period. The Fund
covers these costs only after the facility's post-closure period.
Fund Revenues. Fund revenues come from a tax of $2.13/dry-weight ton
of disposed hazardous waste. This tax rate is fixed in nominal terms, as is
the Fund ceiling of $200 million. The total amount of disposed waste is
assumed to decrease at a rate of approximately 2 percent per year. Therefore,
the total potential annual tax revenue also decreases at this rate. The Fund
may also obtain revenues by investing the positive Fund balance in federal
securities. A nominal rate of return of 5.1 percent per year is used.
In addition to these PCLTF policies, a Superfund coverage policy was
specified. At facilities that do not qualify for the PCLTF and when the
owner/operator or a financial assurance mechanism is unavailable, the
Superfund is simulated to cover certain costs. These costs include on- and
off-site non-routine response actions and natural resource damages.
4.1.2 Simulation I Results
The results of Simulation 1 are presented in a series of graphs and tables
bound separately for the reader's convenience in Volume II of this report.
These results, and the policy simulation results described below, are based on
runs of 29 iterations (i.e., each run is composed of 29 separate estimates of
each quantity of interest). For example, there are 29 estimates of the Fund
balance in year 35 (and in every other year for which the Model is run).
These 29 estimates form a distribution reflecting the combination of the
uncertainties surrounding the various factors influencing the PCLTF. In the
results presented below, these distributions are described in terms of the
following statistics:
• Minimum: the smallest of the 29 values observed.
• Tenth Percent!le: the value below which 10 percent
of the observations fall. For 29 observations, the
third-smallest observation is the tenth percentile.
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4-5
• Median: the value for which there is a 50 percent
chance of an observation falling either above or below
it. For 29 observations, the 15th-ranked observation is
the median.
• Ninetieth Percentile: the value below which 90
percent of the observations fall. For 29 observations,
the 27th-ranked observation is the ninetieth percentile.
• Maximum: the largest of the 29 observations
observed.
• Mean: the average of the 29 observations.
• Standard Error: the standard error of the mean is
computed as the standard deviation of the 29
observations divided by the square root of 28 (i.e.,
N-l). The standard error reflects the variability in
the estimate of the mean.
Each graph and ftable comprising the results is described separately.
Graph l-l: ] Fund Balance. Six lines displayed on this graph present
estimates of the unobligated PCLTF balance2 over the next 44 years. (Note
that while the Model is run for 100 years, this graph shows only 44 years in
order to achieve resolution adequate to see clearly the differences among the
six lines.) The horizontal axis is time, beginning with year 1, which is
fiscal year 1984. The balance (in millions of dollars) is displayed on the
vertical axis, r Starting from the bottom line in year 44, the six lines are
the minimum, tenth percentile, mean, median, ninetieth percentile, and the
maximum (the ninetieth percentile and the maximum are so close together that
they appear as one line). These six lines show that by year 14, the
unobligated PCLTF balance may become and remain negative. The median estimate
has a value of approximately $200 million by year 44. As is evidenced by the
ninetieth percentile and maximum, there is the possibility that by year 44,
the unobligated balance may remain above $1.5 billion.
Graph 1-2: Fund Balance. This graph presents results for Simulation 1
out to year 100. The first 44 years are identical to those presented in Graph
1-1. The same six values are presented, only the scale of the graph is
changed. After year 44, it can be seen that the median estimate becomes
negative by year 56. This means that there is at least a 50 percent chance
that by year 56, the Fund will be in debt. The worst case is represented by
2 The unobligated balance is relevant because the trigger that turns the
tax on and off is defined in terms of the unobligated balance. CERCLA does
not define this concept. Of course, the unobligated balance equals the total
balance minus obligations. For the purposes of the runs reported here,
obligations are defined as the average of the past three years of outlays.
The Model offer£ several options for this, definition.
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the minimum observation, which shows a debt of over $22 billion by year 50 and
over $500 billion by year 100. Of note is that the ninetieth percentile
unobligated balance is negative in year 100. The maximum estimate, however,
exceeds $10 billion in year 100.3
Table l-l: Summary of Facility Population. The facility population is
divided into the following categories (reading from left to right in the
table):
• Operating Existing Facilities: facilities in operation
before year 1 of the simulation that are accepting
hazardous waste.
• Operating New Facilities: facilities simulated to open
during the Model run that accept hazardous waste for
land disposal.
• Closed, Permitted Facilities with Qualification Status
Pending: facilities that are in the five-year
qualification monitoring period awaiting a determination
regarding their ability to qualify into the Fund.
• Closed, Permitted, Qualified Facilities: Closed
permitted facilities that have transferred their
liabilities to the PCLTF.
• Closed, Permitted, Unqualified Facilities: Closed
facilities that were disqualified from PCLTF coverage
because of a release or because their financial
assurance for post-closure care was inadequate.
• Closed, Non-Permitted Facilities with Qualification
Status Pending: Facilities waiting for a qualification
determination that were denied final RCRA permits and
consequently required to close.
• Closed, Non-Permitted, Qualified Facilities: Closed,
non-permitted facilities that have transferred their
liabilities to the PCLTF.
• Closed, Non-Permitted, Unqualified Facilities:
Facilities that were denied final RCRA permits and
consequently required to close.
3 Although these numbers appear very large, they are reported in current
(i.e., inflation adjusted) dollars. For example, in today's dollars, a $10
billion surplus in year 100 is only about $200 million (which is the current
fund ceiling). Consequently, the results demonstrate that in real terms, the
Fund is not likely to exceed the current ceiling in the long term.
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• Closed, Decontaminated Facilities: Facilities which,
when they close, decide to remove all wastes rather than
maintain the facility as a disposal facility during the
post-closure period.4
Table 1-1 displays mean values for the 29 iterations of the Simulation 1 run.
Of the 982 existing facilities,5 253 are simulated to not desire final
permits. By year 50, 211 facilities are simulated to be covered by the Fund
and 26 facilities are simulated to have their qualification status pending.
The total number of operating facilities declines over time because: (1) the
demand for waste disposal is assumed to decline; (2) the average capacity of
new facilities is assumed to grow by.1 percent per year; and (3) there is
assumed to be excess disposal capacity in year 1 of the Model run. The quick
decline in the; number of operating facilities over the first five years is
attributed to facilities that do not receive final RCRA permits and close
immediately.
Table 1-2: Summary of PCLTF Financial Statistics. The mean values of
the key PCLTF financial statistics are displayed in this table. Of note are
the negative fund balance and unobligated fund balance, even by year 50. It
should be notecl that the values presented in this table are in current year
dollars, i.e., they are influenced by inflation. For example, the growth in
non-interest expenditures is, in part, due to inflation. One may question how
both interest payments and interest revenues can be reported for a single year
(e.g., year 40). This occurs because the results represent means across 29
iterations. If several iterations have interest payments, then the average
interest payment will be negative. Similarly, different iterations may have
interest revenues. Consequently, the averages across all iterations will show
both interest payments and revenues. The average net interest payment/revenue
may be calculated by adding the two means (e.g., in year 40, the average net
interest payment across all 29 iterations is: -35.21 + 38.53 = 3.32 million
dollars).
Table 1-3, Table 1-4, Table 1-5. These three tables provide more
detail on the distributions of the key PCLTF financial statistics. The tenth
percentile, mean, standard error of the mean, median, and ninetieth percentile
are presented for each of the six values. Table 1-3 shows that the tenth
percentile, mean, and median balance and unobligated balance are negative in
year 60. Without changing the Fund, the ninetieth percentile estimate shows
the Fund will 'likely be negative by year 100. Table 1-4 shows that in many
years, taxes are not collected because the unobligated balance exceeds the
$200 million Fund ceiling. Table 1-4 is also useful for comparing tax ,
revenues and expenditures. By year 50, the median expenditure climbs above
* Decontamination only occurs at very small facilties and is not
permitted to take place if groundwater contamination is simulated.
5 The average number of existing facilities simulated in the Model can
be calculated by adding across the first row of the table:
982 = 938 + 1 -1- 36 + 7.
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4-8
the maximum possible annual tax revenue ($14.1 million) and never again drops
below it. This is consistent with the negative balance observed in Table
1-3. This comparison of the time profile of revenues and expenditures is
useful for considering questions of Fund adequacy.
Table 1-6: Summary of Annual PCLTF Expenditures. This table presents
the components of PCLTF expenditures over time. Of note is that these
expenditures are in millions of 1982 dollars and are therefore not influenced
by assumptions regarding inflation. The largest individual costs are found in
the claims column. As can be seen, some years have large average annual
claims expenditures. These values "jump around" because the large claims
occur as infrequent shocks to the Fund. The other non-interest costs are a
much smoother increasing function over time. Finally, interest payments,
which are solely a function of the Fund balance, become very substantial (on
average) due to the large negative balances observed in several of the
iterations.
Table 1-7: Frequency of Releases. Table 1-7 describes the releases
that the Model simulates. The table is divided into four parts (7A, 7B, 7C
and 7D), with each part presenting results for the seven release types over
time.6 Table 1-7A summarizes the number of releases expected (X), simulated
6 The seven release types (described in Chapter 2) are as follows;
* IP On: change in indicator parameters at the on-site
monitoring well.
* Detectable On: detectable concentration of
constituents at the on-site monitoring well.
• Toxic On: toxic concentration of constituents at the
on-site monitoring well.
• Taste/Odor: a concentration of constituents resulting
in a detectable taste or odor at the off-site potable
well or naturally-occurring body of surface water
closest to the facility boundary.
• Detectable Off: detectable concentration of
constituents at the off-site potable well or
naturally-occurring body of surface water closest to the
facility boundary.
• Toxic Off: toxic concentration of constituents at the
off-site potable well or naturally occurring body of
surface water closest to the facility boundary.
• Bathtub: the filling up of the facility with water
leading to contaminated surface runoff. This effect is
similar to a bathtub filling with water and overflowing.
Of note is that this release is not simulated to occur.
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(S), and detected (D) for all the facilities examined in the Model. Expected
releases are those which are anticipated to occur based solely on the analysis
of the release data inputs to the Model. Simulated releases, which will
always be less than or equal to expected releases, are those releases which
actually take place in the Model. Most expected releases are simulated to
take place, although some are prevented from taking place by response actions
which are simulated to be taken. Finally, detected releases are those which
the Model estimates will be discovered. A simulated release may go undetected
if monitoring actions are not taken at the facility.
Of note in Table 1-7A is the number of releases simulated to have occurred
before year 1 (i.e., the top row in the table). Of the 980 existing
facilities, nearly one-half (458) are simulated to already have had release
type 2 (detectable concentration on site).
Tables 1-7B, 1-7C, and 1-7D provide release information for facilities.
with qualification status pending, qualified facilities, and unqualified
facilities, respectively. Of note in Table 1-7C is the fairly small number of
detected releases (particularly off-site) which are covered by the Fund. For
example, by year 50, only 12 Type 1 releases (change in indicator parameter on
site) are detected at the 211 qualified facilities. There are far more
releases detected at unqualified facilities (see Table 1-7D). The
qualification requirements clearly weed out many facilities with releases, as
would be anticipated.
Table 1-8, Allocated Costs. Table 1-8 presents the total costs
allocated over time to owner/operators, states, the Superfund, and the PCLTF.
Costs are presented in millions of 1982 dollars and are divided by cost type.
The table is divided into three parts, 8A-, SB, and. 8C, which present costs for
facilities with qualification status pending, qualified facilities, and
unqualified facilities, respectively. By comparing these three parts of the
table, it is clear that costs at PCLTF-qualified facilities are only a small
portion of total costs at all the facilities.
It should be noted that the estimates of personal injury claims are not
based on estimates of increased risk of.disease caused by releases from
hazardous waste disposal facilities. As described in Chapter 2, the claims
estimates reflect the potential magnitude of claims that may be brought by
individuals who believe they have been harmed. The claims are based on costs
associated with, the background rates of certain diseases in the general
population. Because it is generally very difficult or impossible to identify
the causes of these diseases, and because the background incidence is much
larger than the incremental incidence likely to be caused by the relevant
types of potential exposures, the magnitude of potential claims is modeled
based on the background-incidence-induced costs associated with the selected
diseases. If the Fund covers these cost types, the claims may be of the
magnitude estimated here because the inability to establish causality makes it
impossible to separate background disease incidence from incremental incidence.
Table 1-9, Allocation of Monitoring, Response, and Post-Closure Care
Costs. Table 1-9 displays how the monitoring, response, and post-closure
care costs are shared among owner/operators, PCLTF, the Superfund, and
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4-10
states. Again, the table is divided into three parts by the qualification
status of facilities. Only Table 1-9B, Qualified Facilities, shows PCLTF
expenditures.
Table I-10, Allocation of Claims Costs. Table 1-10 is analogous to
Table 1-9, but displays the four claim types. Again, only Table 1-10B,
Qualified Facilities, shows PCLTF expenditures. Of note is that personal
injury claims are simulated to be the largest claim type.
Table l-ll, Summary of Facility/Firm Bankruptcy Status by Year. Table
1-11 shows the combined effect of routine business failures and business
failures induced by the costs of monitoring and response actions and claims.
The table is divided into four main sets of columns, with each set displaying
the results for a size category of firm. For example, the first set is for
facilities owned by firms with a net worth of less than $10 million. At the
beginning of year 1, the table shows that there are (an average of) 243.9
facilities owned by firms in this net worth category. During the first year,
firms owning a total of 11.59 facilities (on average) go out of business.
Consequently, 232.3 facilities are owned by solvent firms in the following
year. This 11.59 facilities going bankrupt out of 243.9 equals a bankruptcy
rate of 4.76 percent, also shown in the table.
Table 1-11 shows a very high bankruptcy rate for small firms (with net
worths under $10 million). During the first 10 years (when permits are issued
and corrective actions are initiated), the bankruptcy rate averages over 7.5
percent per year. This is almost 20 times the historical background
bankruptcy rate used for "these firms (0.42 percent per year). The high rates
are caused by the costs allocated to the firms.
The larger firms (greater than $10 million in net worth) have lower
bankruptcy rates. These lower rates are caused both by their lower historical
background rates (0.26 percent per year) and because larger firms are better
able to handle the costs allocated to them. These results include the fact
that larger firms are more likely to own multiple facilities (thus exposing
them to larger costs).
Table 1-12, Average Occurrence of Releases. This table summarizes the
timing and frequency of releases simulated to occur at all facilities included
in the Model, not just those covered by the Fund. The table is divided into
two parts, existing facilties and new facilities. Each part displays
information about the seven release types for facilities that have been
analyzed for at least 50 years or at least 75 years. For example, under the
"50 years since opening" section, 0.45 (i.e., 45 percent) of existing
facilities had release Type 1 (change in indicator parameter on site). On
average, it took 4.6 years for this release to occur at these facilities.
Facilities that have been analyzed in the Model for less than 50 years are not
included in this estimate. Because all existing facilities were analyzed for
the entire Model run (100 years), all existing facilities are included in the
50-year and 75-year estimates.
The new facilities segment of this table shows virtually no releases.
Virtually no releases are simulated because new facilities are modeled to be
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4-11
opened only at, locations that are in the one-third of all locations least
likely to have a release.
Table 1-13, Summary of Costs. Table 1-13 summarizes the cost estimates
simulated in the entire Model run. For each type of action (monitoring,
response, and routine care) and for each claim type, the following information
is presented:
• Number of Observations: the number of times the Model
estimates the cost in the entire Model run;
Minimum:. smallest cost estimated;
I
Mean: average cost estimated; and
Maximum: largest cost estimated.
For the actions, the fraction of times that each action could not be taken due
to lack of funding is also reported. This lack of funding occurs when neither
the owner/operator, the PCLTF, the Superfund, nor a state fund is available to
pay for the action. The owner/operator may be out of business, the facility
may not be qualified for Fund coverage, the particular type of cost may not be
covered by the Superfund, and the facility may be located in a state without
an applicable state funding mechanism. For example, 1-7 percent of the time,
it was not possible to undertake monitoring for indicating parameters on
site. For this percentage of time, the action was not taken.
4 For the claims, the fraction of generated claims determined not to be
legally valid is reported. These estimates reflect the portions of the claims
that are simulated not to be legal liabilties of the facility under existing
state and federal law. Additionally, the fraction of claims not compensated
is also reported. Claims are not compensated when neither the owner/operator,
PCLTF, Superfund, nor state funds are available to pay the harmed parties.
The graphs and tables described above form the quantitative output from
the PCLTF Simulation Model. These results of the modeling exercise provide an
internally-consistent estimate of the potential future performance of
hazardous waste land disposal facilities and the PCLTF. In interpreting these
results and drawing conclusions, one must look beyond the numbers themselves
to understand the important factors and assumptions that drive the results.
By using the results in this manner, the PCLTF Simulation Model provides a
basis for identifying the key controlling factors in the complex PCLTF
situation.
The following section discusses conclusions regarding PCLTF adequacy based
on the Simulation 1 results.
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4.1.3 PCLTF Adequacy in Simulation I
To assess the adequacy of the PCLTF, a clear definition of the concept of
adequacy is required. Although a variety of quantitative adequacy measures
may be used, each is somewhat arbitrary in its consideration and evaluation of
various factors. Consequently, a qualitative approach for assessing the
adequacy of the PCLTF is adopted here.
For this discussion, the PCLTF is considered adequate for the purposes
described in CERCLA if Fund revenues are likely to equal or exceed Fund
expenditures over the foreseeable future. Because there is considerable
uncertainty about both future revenues and expenditures, this assessment of
adequacy will itself be uncertain. Just how certain one must be in order to
proclaim that the Fund is "adequate" depends on the decision-maker's
perception and evaluation of the risks of being incorrect in the
determination. Being incorrect could result in a Fund that is either
inadequately funded or with very large positive balances. Neither prospect of
being incorrect is attractive.
Because of the subjective nature of the adequacy determination, the
emphasis here is on the insights that may be gained from the quantitative
model results. Although the authors' opinions are expressed below, the final
judgment clearly resides with the appropriate decision-making authorities.
The model results for Simulation 1 indicate:
* There is less than a 50 percent chance that the PCLTF
will go into debt over the next 50 years, although a
substantial potential exists. The potential for
expenditures exceeding revenues within the next 50 years
is caused by the possibility of one or more large
third-party claims being brought against the Fund during
that time. The solvency of the Fund is sensitive to the
large "shocks" or peaks caused by the advent of these
claims.
• The long-term outlook for the Fund indicates
expenditures consistently exceeding potential tax
revenues, resulting in negative Fund balances when
interest revenues are insufficient to make up the
shortfall. The possibility of having a solvent Fund
over the long term without making changes in its funding
or expenditure policies appears to be very unlikely (the
ninetieth percentile estimate is negative by year 100).
• The Fund is not likely to build up a large, unused
positive balance. The ninetieth-percentile estimate of
the balance only reaches $3 billion (near year 60) and
becomes negative by year 100. Although the maximum
estimate of the balance exceeds $10 billion by year 100,
this balance is" only on the order of $200 million in
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1982 dollars (using the inflation rates adopted in the
Model).
• The potential debt of the Fund is very large. The
tenth percentile estimate of the balance in year 100 is
a debt of nearly $245 billion. The minimum estimate in
year 100, a debt of over $500 billion, is nearly $9
billion in 1982 dollars.
Exhibit 4-2 compares non-claims Fund expenditures to potential tax
revenues over the first 80 years. As the exhibit shows, it is very likely
that expenditures will routinely exceed potential tax revenues, even when the
shocks from claims expenditures are omitted. These costs exceed potential tax
revenues because: (1) the tax base is assumed to decline over the first 50
years of the simulation; (2) the tax rate is kept fixed in nominal terms at
$2.13 per dry-weight ton; and (3) costs increase with inflation. When
compared in real terms, (i.e., if the tax rate were held constant in real
terms) the potential taxes would exceed the non-claims costs throughout the
entire period examined. Consequently, increasing the tax rate to keep pace
with inflation would certainly provide sufficient funds to cover all
non-claims costs under the assumptions used in Simulation 1. In fact, as
discussed in Chapter 5, keeping the tax rate constant in real terms is
sufficient (on, average) to cover all the PCLTF-covered costs simulated in
•Simulation 1. !
As the results also show, interest revenues can be an important source of
funds (see Table 1-5). The real interest rate used in Simulation 1 is
approximately 1 percent per year. This assumes that the future real return on
government securities is the average annual real return from 1960 to 1982 for
3-month, 6-month, 3-year, and 10-year U.S. Treasury securities (when compared
to the Consumer Price Index). Over this time period, the longer-term
securities had, returns in excess of this average, while the short-term
securities had]below-average yields. Insofar as future real returns from
these securities (in which the Fund would be invested) exceed the rate used
here, the interest revenues earned by the Fund may be underestimated. Of
course, the advantages and risks of counting on uncertain future interest
revenues as a primary funding source for a fund such as the PCLTF must be
considered carefully.
Based on the evidence from the PCLTF Simulation Model and the insights
discussed above, it appears that the PCLTF, as represented in Simulation 1,
will likely have sufficient revenues to cover potential costs over the next 20
to 40 years. After that time, expenditures are likely to exceed revenues, in
part because the tax rate is fixed in nominal terms. If the tax rate were
held fixed in real terms, it is likely that the Fund could remain stable
throughout the 100-year period examined (alternative revenue policies are
discussed more fully in Chapter 5). The next section presents results for
four alternative simulations.
-------
4-14
EXHIBIT 4-2
COMPARISON OF PCLTF NON-CLAIMS
EXPENDITURES TO POTENTIAL PCLTF TAX REVENUES
200
MILLIONS
OF CURRENT
DOLLARS
150
100
50
Rang* of Cost*
_ Potential Tax R«v«nu«
20
40
60
80
100
YEAR
-------
4-15
4.2 POLICY SIMULATIONS
This section presents the results of four policy simulations using the
PCLTF Simulation Model. The four simulations (Simulations 2 through 5) are
each defined in terms, of how they differ from Simulation 1. Then, the manner
in which the results of each simulation differ from the Simulation 1 results
is described. Finally, the distribution of costs among potential funding
sources is discussed.
4.2.1 Definition of the Four Policy Simulations
Exhibit 4-3 displays how each of the policy simulations differs from
Simulation 1. These differences relate to Fund coverage and projected waste
volume. Only the aspects of the policy simulations which differ from
Simulation 1 are displayed in Exhibit 4-3. A blank space in the exhibit
indicates that the same value was used for both the policy simulation and
Simulation 1. ,;The policy simulations are as follows:
Simulation 2. Partial Ban on Land Disposal. In Simulation 2,
wastes are modeled to decline by 50 percent over the first 10 years
of the Model run. Waste disposal continues to decline by 2 percent
per year through year 50. In Simulation 1, waste disposal declines
by 2 percent per year through year 50. In all other respects,
Simulation 2 is identical to Simulation 1.
Simulation 3. PCLTF Coverage of Non-Routine Monitoring and Response
Costs Following Qualification. In Simulation 3, the costs of
non-routine monitoring and response actions are covered by the PCLTF
during the 30-year post-closure period at qualified facilities.
Simulation 4. Elimination of Claims from PCLTF Coverage. This
simulation eliminates third-party claims from PCLTF coverage. The
claims costs are allocated to owner/operators and state funds as they
are available. The allocation of large claims to owner/operators
forces them to terminate business in some situations.
Simulation 5. No PCLTF. Simulation 5 analyzes the situation
assuming that the PCLTF does not exist. At facilities that would
otherwise be eligible for PCLTF coverage, the Superfund is simulated
to cover the costs of natural resource damages after closure as well
as response costs starting 30 years after closure. All other third
party claims are simulated to be the responsibility of the
owner/operator. If the owner/operator cannot meet the costs of third
party claims, a state fund may cover these costs, otherwise they
remain unfunded. State coverage of routine monitoring, maintenance
and care is simulated beginning 30 years after closure. If a state
fund is not simulated to exist to cover these routine costs, then the
actions are not taken. All other assumptions about funding sources
and the conditions under which payments will be made are the same as
Simulation 1.
-------
4-16
EXHIBIT 4-3
SUMMARY OF SIMULATIONS
SIMULATIONS
POLICY
Qualification
Monitoring Period
1 2
5 years
i
Releases |None allowed
1
Financial Assurance (Must be adequate
(during qualification
[monitoring period
(5 years)
RCRA Permit Required! Yes
1
Coverage j •
1
Routine Monitoring (After post-closure
and Care (period (30 years)
1
Non-Routine
Monitoring and Care
After post-closure
period (30 years)
"
Third-Party Claims (After qualification
j (5 years)
Fund Revenues
Tax and Ceiling
Fixed in nominal
terms
I
Waste Volume J Decrease by 25 per
(year through 2033
j(i.e., for 50 years)
!
'
Decrease by
505 by 1993,
then 25 per
year through
2033
3 | 4 | 5
After
qualification
(5 years)
-
•
Not
covered
No PCLTF
No PCLTF
No PCLTF
No PCLTF
No PCLTF
No PCLTF
No PCLTF
No PCLTF
-------
4-17
4.2.2 Policy Simulation Results
The results of simulations 2, 3, 4, and 5 are described below. For the
reader's convenience, the graphs and tables for all of the runs are bound in
Volume II together with the Simulation 1 graphs and tables. The following
discussion emphasizes the differences between the policy simulation results
and the Simulation 1 results. Because the interpretation of each of the
output tables ,was presented above for Simulation 1, each table is not
discussed individually for all the policy simulations. The reader may find it
useful to refer back to Exhibit 4-3 for the definition of how the simulations
differ from Simulation 1.
Simulation;2. Partial Ban on Land Disposal. The assumption that waste
.volume declines by SO percent over the next 10 years has two important effects
on the results of this simulation. The first is that the number of new
facilities is much lower than in Simulation 1. By year 50 in Simulation 1,
there are 217 operating facilities, as Table 1-1 shows; in Simulation 2, there
are only 138 (see Table 2-1). Although there are many fewer new facilities,
the total costs of response and monitoring actions and claims are only
slightly reduced during the period examined. This is because most costs occur
at existing facilities. As seen in Tables 1-7A and 2-7A, there are
approximately ,the same number of releases in the two runs. This result is due
to the fact that new facilities have very few releases.
The second- important impact of decreasing wastes is its effect on tax
revenues. Because the volume of wastes determines the tax base, declining
wastes imply less tax revenues to the Fund. This is evident in Table 2-4
where, after 50 years, tax revenues are, at a maximum, about $8 million per
year. In Simulation 1, annual tax revenues may exceed $14 million after year
50.
Simulation 3. PCLTF Covers Non-Routine Monitoring and Response Costs
Following Qualification. The impact of this policy is to increase PCLTF
expenditures on response actions. In this simulation, these actions are
covered by the PCLTF rather than by owners/operators, the Superfund, and state
funds. By year 50, these expenditures are approximately three times the
levels in Simulation 1 (see Table 3-6 and Table 1-6). By year 100, the
difference is approximately two times.
The long-term outlook for this Fund configuration is similar to that of
Simulation 1. Although expenditures for response actions are considerably
higher, claims: continue to dominate total expenditures by the Fund. Claims
are the major factor leading is a large Fund debt.
Simulation 4. Elimination of Claims from PCLTF Coverage. By
eliminating third-party claims from Fund coverage, large expenditure shocks
are avoided. Additionally, there can be no PCLTF expenditures over the next
30 years (because non-claim costs are only coveraged after the end of the
post-closure period). As a consequence, a large positive balance can be built
up which yields substantial interest revenues.
-------
4-18
A comparison of Table 4-1 and Table 1-1 shows that the facility
populations are nearly identical for the two runs. Also, Table 4-6 and Table
1-6 show that the monitoring, response, and post-closure care expenditures are
nearly equivalent. Consequently, the only difference between the runs is the
elimination of coverage for third-party claims.
Simulation 5. No PCLTF. Because the PCLTF is not included in this
simulation, the output tables presented in Volume 2 contain no information on
the Fund's financial performance. For those tables that provide information
about facilities based on Fund qualification status, results are presented for
facilities which would have been pending, qualified, or unqualified using the
assumptions about qualification from Simulation 1. Because this simulation
omits the PCLTF, facilities do not actually have a qualification status and
there are no Fund payments at any facility.
The population of facilities over the next 100 years is quite similar to
that estimated in Simulation 1. For example, by year 100 there are estimated
to be a total of 1,234 facilities (both open and closed), in both simulations.
The primary effect of omitting the PCLTF is a change in both the level of
funds available to cover costs at land disposal facilities and the mix of
sources providing such funds. In general, at facilities which would have
qualified for PCLTF coverage, personal injur-y and property damage claims
become the responsibility of owners and operators, or in the event of
bankruptcy, a state responsibility. Claims for natural resource damages at
these facilities become the responsibility of the Superfund, with subsequent
cost recovery from owner/operators if possible. Similarly, should a required
corrective action go unfunded at some point after the post-closure period has
ended, Superfund action would be expected. Finally, because owner/operators
are responsible for the costs of routine monitoring and care only in the 30
years after closure, these costs would go unfunded after 30 years in the event
that state monies were unavailable.
4.2.3 Distribution of Costs
This section describes the allocation policy used for Simulation 1 and the
differences between it and the policies used for Simulations 2 through 5. The
results of all five simulations are examined to determine variations in the
cost burden faced by different funding sources. The method by which the Model
allocates these costs among different funding sources is explained in Section
2.3.2.4, Allocation Policies.
Exhibit 4-4 presents the allocation policy used for Simulation 1. The
intent of the policy is to reflect EPA's interpretation of current statutory
and regulatory requirements concerning the funding sources that are
responsible for different types of costs over a facility's lifetime. In the
PCLTF Model, two of these funding sources, the owner/operator (including its
financial assurance mechanism) and the states, may be simulated to be unable
to cover a particular cost. In such cases, the Model looks for another party
in the chain of responsibility to cover the cost. The other two funding
sources, the Superfund and the PCLTF, are simulated to be fully adequate to
cover any cost allocated to them. Exhibit 4-5 presents the differences
-------
EXHIBIT 14-I»
ALLOCATION OF COSTS AMONG FUNDING
SOURCES BY TYPE OF COST i/
Funding Source
Rout ine Monf toning
and Care
Owner/Operator 2/ During operation
and 30 years after
closure
PCLTF
States
Superfund
Beginning 30 years
after closure, if
qua I if led
After closure, if
other sources fail
Not responsible
... On-Site ..
Response
During operation
and 30 years after
closure
Beg i nn i ng 30 yea rs
after closure, i f
qua I i f led
Off?Site, , ...
Response Action
During operation and
30 years after closure
Beginning 30 years
after closure, i f
qua Ii Tied
Th i rd-Pa rty
Cla ims
Not
responsible 3/ Not responsible 3/
During first 30
years after closure,
i f owne r/ope ra to r
fa iIs; if 30 years
after closure, has
fult responsibiIi ty
if not qua I ified
for PCLTF
During first 30
years after closure,
if owner/operator
faiIs; if 30 years
after closure, has
fulI responsibiIity
if not qua Ii fied
for PCLTF
In perpetui ty.
except i f qua Ii f ied
for PCLTF
After closure,
qua Ii fied
if
After closure, i f
owner/operator fails;
and i s not qua Ii f ied
for PCLTF; all cla ims
other than natural
resource damages
After closure, for
natural resource
damages only
i/ For Simulation I.
2/ Includes owner/operator financial assurance mechanism.
I/ States are assumed to pay for 10 percent of Superfund expenditures at privately-owned facilities and
50 percent at state- and municipally-owned facilities.
-------
4-20
EXHIBIT 4-5
DIFFERENCES IN ALLOCATION OF
LAND DISPOSAL COSTS FOR SIMULATIONS 2 THROUGH 5
Simulation Differences from Simulation I1
2 - Partial Ban on Land Disposal No difference in cost allocation policy.
3 - PCLTF Coverage of Non-Routine PCLTF covers both on-site and off-site
Monitoring and Response Costs responses between 5 and 30 years after
closure, if facility qualified.
4 - Elimination of Claims from PCLTF does not cover claims; if owner/
PCLTF Coverage operator fails, Superfund covers natural
resource claims, states cover (if
possible)'all other claims.
5 - No PCLTF PCLTF eliminated as a funding source.
1 Only differences from Simulation 1 are presented. If a particular
type of cost or funding source is not presented, the policy is the same as in
Simulation 1.
-------
4-21
between the allocation policy used for Simulation 1 and those used in
Simulations 2 through 5.
The effect of the different allocation policies on the distribution of
costs is shown in Exhibit 4-6, Of note is the fact that there is very little
difference among the results for the simulations. This is because the PCLTF
covers such a small fraction of all costs. Variations in PCLTF policy have
very little effect on the overall distribution of costs. In all simulations,
about 13 percent of the costs associated with land disposal facilities are not
covered by any funding source. Slightly more than 80 percent of the costs are
covered by owner/operators or their financial assurance mechanism. The
remainder of the costs are simulated to be borne by the states or the
Superfund.
4.3 CONCLUSIONS
This chapter has described initial estimates of the future PCLTF balance
for four alternative Fund configurations. Also, a fifth configuration, No
PCLTF was examined. The estimates are based on the PCLTF Simulation Model
which was developed for the purpose of assessing PCLTF adequacy. The Model is
limited in a variety of ways which are discussed in Chapter 6. Many of its
limitations are inherent in virtually all models seeking to project far into
the future. As these results are circulated and reviewed, adjustments to
various aspects of the Model will likely be forthcoming. Nevertheless, the
insights gained from the results presented here are valuable in their own
right, and should prove to be useful in the debate surrounding the appropriate
configuration for the PCLTF.
Based on the results presented above, the following insights may be
obtained:
• As configured in Simulation 1, the PCLTF revenues would not be
adequate for ensuring Fund solvency in the long term (over the
next 100 years). However, there appears to be no need for
concern over the next 20 years because major Fund expenditures
remain 'years away.
• The ability to raise revenues through a waste-end tax in the
long term is very uncertain. As shown in Simulation 2,
restrictions on future land disposal or the introduction of new
treatment and process technologies may reduce future taxable
waste substantially (without significantly reducing PCLTF
expenditures over the same time period).
• These analyses have identified the fact that over the next 50
years the Fund is likely to have a positive balance, making the
Fund "adequate." However, the question of Fund adequacy can also
be addressed in terms of whether the Fund is meeting its stated
objectives. Exhibit 4-6 shows the relative contribution of the
funding sources. As the exhibit shows, the overwhelming majority
of expenditures is made by Superfund and owner/operators. The
PCLTF pays very little (as a fraction of the total).
-------
EXHIBIT M-6
DISTRIBUTION OF COSTS AT LAND DISPOSAL FACILITIES
OVER 100 YEARS WITH 6% DISCOUNT RATE i/
(in millions of 1982 dollars)
Fundinq Source
PCLTF
Owner/Operator 3/
Superfund; States
Total costs covered
Total costs not covered U,
Total costs
Simulation I :
Current PCLTF
Percent of
Costs Total Costs
89 )
8,291 82
370 4
8,750 87
' 1,282 13
10,032 100
Simulation 2:
Partial Ban
on Land Disposal
Percent of
Costs Total Costs
93 1
8, I7i| 81
383 t
8,650 86
1,382 m
10, 132 5/ 100
S i mu 1 a t i on 3 :
Coverage of
Corrective Action
Percent of
Costs Total Costs
95 1
8, '457 8U
382 14
8.93U 89
1,098 M
10,032 100
S i mu I a t i on 'i ;
El imination of
3rd Party Cla ims
from PCLTF
Percent of
Costs Total Costs
5 * 2/
8,325 B3
374 U
8,704 87
1,328 13
10,032 100
Simulation 5:
No PCLTF
Percent of
Costs Total Costs
0 0
8,571 85
378 4
8.9U9 89
1,083 1 1
10,032 6/ 100
I
M
NS
i/ Costs include routine monitoring and care, on-site and off-site response actions and third party claims during operating
life and after closure.
2/ Less than .5%.
3_/ Includes owner/operator financial assurance mechanism.
{»/ The total costs not covered by any funding source was computed fn two steps. First, a supplementary Model run was
performed using an allocation policy providing full funding for all costs at all times. This provided an estimate of total costs
of $J0.032 billion. Second, the total covered cost for each simulation was subtracted from $10.032 billion to yield an estimate
of total costs not covered. This estimate may be a lower bound because in the absence of a funding source, an action will not be
taken immediately. When it is ultimately taken, its cost may be higher. Thus, total costs not covered, and consequently total
costs, may be higher than indicated for each simulation.
5_/ May be an overestimate of total costs because there are many fewer new facilities in Simulation 2.
£/ May be an underestimate of total costs because without the PCLTF some actions may not be taken that prevent subsequent
releases from occurring. Consequently,' the estimate for costs not covered may also be an underestimate.
-------
4-23
Consequently, as configured in Simulation 1, the Fund plays a very
minor role in protecting the environment and human health and welfare
(relative to other sources). Because Fund coverage is so limited,
the Fund may provide little or no incentive for owner/operators, as
envisioned' in the drafting of the legislation. Therefore, the Fund
as currently configured may not be meeting its objectives. Also,
small modifications in the Fund's qualification or coverage policies
could result in substantial increases in Fund expenditures, thereby
causing the Fund to no longer be solvent.
-------
(PAGE INTENTIONALLY LEFT BLANK)
-------
CHAPTER 5
REVENUE ANALYSIS
5.1 INTRODUCTION
The previous chapter presented the results of several policy simulations
of the PCLTF Model. In this chapter, we examine ways in which the Fund might
be managed to achieve certain policy goals. Such goals might include
attaining a desired reserve in the Fund or setting a tax rate in year 1 that
will remain unchanged over time and will yield a given balance in year 100.
Fund managers can seek to achieve these and other goals by varying several
revenue parameters that relate to the management of the Fund.
As a supplement to the PCLTF Simulation Model, a revenue analysis module
has been developed to permit a detailed analysis of the Fund's revenue
parameters. Once a given expenditure policy.has been simulated using the
Model, the revenue analysis can be used to evaluate different revenue
policies. This module can test various rules for the operation of a Fund
floor, a Fund ceiling, and Fund tax policy. The output of this module
includes estimates of the tax rate and Fund balance over time as well as other
financial variables of interest. Like the PCLTF Simulation Model, -its output
consists of results for each of 29 iterations and is summarized using the same
statistics discussed in Chapter 4.
The revenue analysis takes as input the simulated expenditure stream from
each iteration of a Model run. Consequently, the revenue analysis does not
simulate interactive effects between revenue policies and any of the events
simulated in the PCLTF Model run. For example, the revenue analysis module
would not simulate the decline in waste volume that might be evoked by an
increase in the tax rate. In other words, the revenue analysis is conducted
on the results.of a single Model simulation, thereby holding the stream of
Fund expenditures constant while allowing other financial parameters to vary.
!!
While the revenue analysis examines Fund rules and tax policies that may
change over time, no distinction is made among the methods used to implement
these changes. For example, some adjustments to funding policies may be made
automatically, as with the provision that suspends the current tax of $2.13
per dry weight ton when the unobligated Fund balance exceeds $200 million.
Alternatively, ^changes could be made at the discretion of EPA or could require
Congressional approval. Rather than focusing on the parties responsible for
setting and adjusting the Fund rules and tax policies, the revenue analysis
simply simulates the financial effects of such changes.
This chapter presents ah overview of the revenue analysis module and
summarizes the xesults obtained from a revenue policy analysis of four of the
five PCLTF simulations discussed in Chapter 4. (A revenue analysis is not
performed for Simulation 5, No PCLTF, because there is no fund.) First, the
revenue analysis is defined, including its inputs and outputs. Next, the
specific revenue policy used in this chapter is presented. This is followed
-------
5-2
by a discussion of results, which include break-even tax rates (defined below)
for each simulation and estimates of the financial parameters of interest.
5.2 DEFINITION OF THE REVENUE ANALYSIS
This section describes the revenue analysis. The types of inputs
necessary to define a revenue policy are presented, followed by an explanation
of the output.
5.2.1 Defining the Revenue Policy
In order to conduct a revenue analysis, a specific revenue policy must be
defined. The policies examined here involve rules governing Fund floor and
ceiling operations, a definition of the tax base and an initial tax rate, and
the means of treating revenue recoveries from potentially-liable parties.
Each of these factors is discussed below.
5.2.1.1 Fund Floor and Ceiling Rules
A Fund ceiling is simply a level of the Fund which, when exceeded by the
actual balance, results in some set of actions being taken. Currently, if the
unobligated PCLTF balance exceeds $200 million, the tax on hazardous wastes is
suspended. A floor operates in much the same way so that when the balance
drops below a given level, some action is taken. (Note that the action(s)
need not be automatic, as is currently the case with the tax switching off
when the ceiling is reached. Non-automatic actions which are reasonably
anticipated to occur can also be modeled.)
Both a Fund ceiling and floor can be useful in modeling the performance of
the Fund, given the large uncertainty regarding future revenues and expenses.
There is considerable uncertainty regarding, for example, the size of the tax
base over time (i.e., the amount of land disposal) and the rate of spending
that will follow from several probabilistic processes (i.e., monitoring,
response, and claims activities). As a result, the balance in the Fund may
deviate from initial expectations. By modeling Fund management actions in
response to the balance exceeding a ceiling and falling below a floor, a
realistic picture of future Fund performance and potential Agency or
congressional 'response can be developed.
The initial floor and ceiling are inputs to the revenue analysis, although
neither is required. If they are used in the Model, the floor or ceiling may
be held constant in nominal terms or may be adjusted for inflation.
The floor or ceiling may also be adjusted using the concept of Fund
reserves. Fund reserves are the number of years of expected spending
represented by the Fund balance. If, for example, the Fund balance is $300
million and expected annual spending is $75 million, reserves are equal to
four years. A floor or ceiling can thus be defined in terms of reserves. For
example, the floor might be set at five years of expected spending. As
spending levels change, the floor would be automatically adjusted. The
revenue analysis module has a series of options that allow the user to control
the timing, frequency, and magnitude of such adjustments. These options serve
-------
5-3
to limit the number of small or "fine-tuning" adjustments that would otherwise
occur. Both the inflation- and reserves-based adjustments may be made in a
single revenue analysis.
5.2.1.2 Tax Rules
An important use of a Fund floor or ceiling is to identify when.
adjustments to the tax base and/or tax rate are required. Both a floor and
ceiling may be used to hold the Fund balance within an acceptable range.
Reaching either threshold may imply the need to adjust one or more of the
factors that affect the balance. Because the revenue analysis is performed
for a given estimate of PCLTF expenditures simulated by the Model, it does not
simulate changes in expenditure policy. Consequently, the module simulates
only changes in the tax base or tax rate. Each is discussed below in turn.
The PCLTF tax is levied on the land disposal of hazardous wastes.
Currently set at $2.13 per dry weight ton, this tax could be levied instead on
the volume of waste disposed or on its wet weight. Either change could be
used to expand the base of material subject to taxation and increase the
amount of Fund'income for a given tax rate. The module can simulate such a
switch in the tax base in response to reaching either the floor or ceiling
(i.e., if the balance is below the floor, the base would be expanded if
possible; if at the ceiling, the opposite is done).
The revenue analysis module is also able to make changes to the tax rate.
The simplest such adjustment is to index the tax rate to inflation. While
this addresses the problem of the general decline in the value of money
brought about by inflation, it does not reflect factors unique to the PCLTF.
A great number ,of forces besides inflation affect not only the market for
hazardous wastes (i.e., the tax base) but also the simulated expenditure
streams of the ,PCLTF.
Either in lieu of or in addition to inflation adjustments, the revenue
policy may call for changes in the tax rate as a function of the Fund
balance. The tax can either be suspended or it can be adjusted downward when
the ceiling is reached. Similarly, if the floor is reached, the tax can be
adjusted upward. As with the floor and ceiling adjustments, the user can
limit the magnitude, frequency, and timing of tax rate changes.
The revenue analysis module has an added feature, called the "break-even
analysis," which can calculate the tax rate that will produce a balance of a
given size in the final year of the model run. This analysis is independent
of the various potential floor and ceiling adjustments and does not make use
of the tax adjustments just discussed. For each estimate of future PCLTF
spending (i.e.,; the result of each PCLTF Model iteration), this procedure
determines the initial tax rate which, if collected every year, produces the
desired ending balance. This is done by computing the tax rate that results
in sufficient tax revenues being collected so that total Fund revenues
(including both tax and interest revenues) minus Fund expenditures equal the
target balance at the end of the simulation. Results are presented for both
an inflation-adjusted rate and a nominally-constant rate.
-------
5-4
Because the break-even analysis is conducted for each iteration, a series
of rates is computed for each run (a run has 29 iterations). Given that the
tax base and target balance are the same for each iteration, the break-even
tax rate varies across iterations because the simulated PCLTF expenditures
.vary. The mean, median, tenth and ninetieth percentiles of the break-even tax
rates are estimated for each of the four simulations. While the mean or
median may be treated as the best estimate of the appropriate tax rate, there
still remains some residual uncertainty. For example, if the median
break-even tax rate is used (and remains unchanged), there is a 50 percent
chance that the Fund will have more money than expected, and a 50 percent
chance that the Fund will have less money than expected. This uncertainty can
be evaluated by examining.the standard error of the mean along with the tenth
and ninetieth percentiles.
5.2.1.3 Recoverability of Fund Expenditures
Recoverability refers to the authority and ability of the PCLTF to seek
and obtain revenues from parties such as owner/operators, generators, and
insurers as compensation for expenditures made by the Fund. Recoverability is
an important facet of revenue, policy because recovered monies may contribute
significantly to Fund balances. Recoverability is not simulated at the
facility level; instead, it is tracked in the aggregate for each of several
cost types. Different cost types (i.e., routine monitoring, off-site cleanup,
and so on) are treated separately in the recoverability analysis because
economic and regulatory considerations might lead to different recoverability
rates. For example, the Fund might be more successful in recovering the costs
of on-site cleanup than damage claims for natural resources. Further, the
revenue analysis module can incorporate delay factors for recoverability.
Thus, one type of cost might be recovered in two years, while another requires
five. '
5.2.2 Revenue Analysis Outputs
Once the revenue analysis has been completed, several results may be
evaluated. First, the values of important Fund parameters may be examined for
each year of the simulation. Second, information is presented about the
number and magnitude of changes to the floor, ceiling, tax rate, and tax
base. Finally, the break-even analysis presents estimates of the minimum
required tax rate. Each of these outputs is briefly explained below.
Fund Parameters. For a given PCLTF spending profile (i.e., the output
of the PCLTF Model simulation run) and revenue policy (i.e., the inputs to the
revenue analysis module), several key Fund parameters are tracked during the
time horizon of the Model. For each year of the Model run, statistics (the
median, tenth percentile, ninetieth percentile, mean, and standard error) are
presented for each of the following 12 quantities:
• Unobligated balance;
• Balance;
• Non-Interest expenditures;
• Interest payments;
* Tax revenue;
-------
5-5
• Interest revenue;
• Fund floor;
• Fund ceiling;
• Tax rate;
• Tax base (dry vs. wet weight);
• Recoveries; and
• Reserves (in years).
Summary of Changes. Changes in the four adjustable Fund parameters
(the floor, ceiling, tax rate, and tax base) are also summarized to show their
patterns of adjustments. The number of changes to these parameters as well as
the magnitude of the changes are presented in tabular form.
Break-Even Analysts. Because the break-even analysis computes tax
rates for each iteration of a Model run, results must be presented in summary
form. The revenue analysis module thus provides the median, tenth percentile,
ninetieth percentile, mean and standard error of the year 1 tax rate for both
the indexed tax rate and the constant tax rate.
5.3 SIMULATED REVENUE POLICY
This section defines the revenue policy that is evaluated for each of the
first four simulations described in Chapter 4. The purpose of this revenue
policy is to define fully a set of rules to guide Fund operations over the
time horizon of the Model run.1 The results of the revenue analysis then
allow an evaluation of the effectiveness of the Fund management policy.
Defining the revenue policy requires the specification of Fund rules in three
areas: floor and ceiling rules, tax rules, and recoverability rules. Each is
discussed in -turn.
Floor and Ceiling Rules. Initially set at $200 million and $400
million, respectively, the floor and ceiling are adjusted annually by the
inflation rate; Both thresholds are also adjusted based on Fund reserves.
Over time, the floor is adjusted to be equal to five years of expected
spending, while the ceiling is set equal to ten years of expected spending
(expected spending is simulated each year using a weighted moving average).
These reserves-based adjustments are also limited in the following manner: a
single adjustment may not change the floor or ceiling by less than 25 percent
or more than 50 percent; changes are made only after the threshold has been
breached for five or more consecutive years; and changes can be made no more
often than every five years.
Additionally, for the break-even analysis, the target ending balance must
be specified. : In this case, the target for the year 100 balance has been set
at $10.7 billion, which is an amount equivalent to $200 million in 1982
dollars. As noted in Section 5.2 above, the break-even computation is made
1 These rules are only one way of defining a revenue policy. Any number
of policies can be simulated as long as each can be translated into
appropriate model inputs.
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5-6
independently of all other operations in the revenue analysis module. Thus,
the target balance affects only the break-even analysis.
Tax Rules. The tax is set initially at $2.13 per dry-weight ton and is
adjusted annually by the inflation rate. The tax base is not permitted to
change (i.e., the change to a wet-weight basis is not allowed). If the Fund"
balance exceeds the ceiling, the tax is suspended until the balance drops
below the ceiling. When the tax is re-instituted, it is set at the same rate,
adjusted for inflation, as it was when suspended. If the balance drops below
the floor for five consecutive years, the tax rate is adjusted to reflect the
amount of revenue required to reach 110 percent of the floor within five
years. The volume of wastes during this five-year period (i.e., the tax base)
is estimated given the waste projections used in the PCLTF Model simulation
run. The tax may be adjusted no more than once every five years and the
adjustment must be a change of at least 10 percent and no greater than 300
percent.
Recovery Rules. Recoveries are not counted on as a potential source of
funds for this particular revenue policy. There is little available evidence
as to the certainty or magnitude of such recoveries; hence, they are not
simulated.
5.4 RESULTS OF REVENUE POLICY SIMULATIONS
The revenue policy described in Section 5.3 was analyzed for each of the
first four PCLTF simulations presented in Chapter 4. The results of these
analyses are presented in this section. First, an overview of the results is
presented and discussed. Then, the break-even analysis of tax rates is
presented.
5.4.1 Overview of Results
Exhibit 5-1 presents the key financial results for each of eight
scenarios. Each scenario is classified according to the spending simulation
involved and whether the revenue policy reflects current law or the simulated
policy described in Section 5..3.
The results clearly indicate that the simulated revenue policy leads to
stronger Fund performance than the current law. This stronger performance
results from, in general, allowing the tax rate to increase and the fact that
the ceiling is always higher than under current law, thus allowing longer
periods of tax collection. In every case, the simulated revenue policy leads
to a higher mean balance; further, Fund balance estimates are more often
non-negative, at the tenth percentile. Each simulation is discussed in more
detail below.
Simulation 1. Current PCLTF Statute. Under the current PCLTF statute,
Fund performance is substantially better with the simulated revenue policy
than under current law. Two key reasons are that tax rates are permitted to
rise to reflect both inflation and the desire to maintain a balance in excess
of five years of expected spending (i.e., the five-year reserves base).
Additionally, the fact that the ceiling in the simulated revenue policy begins
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5-7
Simulation
1. Current PCLTF Statute
• Current revenue
policy
• Simulated revenue
policy
2. Current PCLTF Statute
with Partial Ban on
Land Disposal
• Current revenue
policy
* Simulated revenue
policy
3. Coverage of Non-
Routine Monitoring
and Response Costs
Following
Qualification
• Current revenue
policy
» Simulated revenue
policy
4. Elimination of Third-
Party Claims
• Current revenue
policy
• Simulated revenue
policy
EXHIBIT 5-1
RESULTS OF REVENUE ANALYSIS RUNS
1st Year
Year in
Mean Mean Which 10th Mean
Balance Balance Percentile Ceiling
in Year 50 in Year 100 Balance is in Year 100
($ millions) ($ millions) Negative ($ millions)
-1,440
2,282
-1,513
2,455
-1,602
1,904
2,029
4,501
-91,654
7,871
-87,288
5,452
-103,141
6,095
8,893
38,593
21
22 I/
21
22
.21
21 2/
91
200
2,773
200
2,521
200
4,430
200
2,660
_!/ The tenth percentile becomes positive again in year 29 before going
negative for the years 40 to 84 and 89 to 100.
2/ The tenth percentile becomes positive again in year 26 before going
negative for the years 51 to 55, 58 to 86, and 89 to 100.
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5-8
at $400 million (twice the current $200 million) and is indexed to inflation
means that taxes may be collected for a longer period of time. Under the
current statute, the ceiling is reached and the tax is suspended in year 6
when the balance is $223 million. In the simulated policy, however, the
ceiling is not reached until year 12 when the balance is $689 million. This
differential, coupled with the interest on the higher balance, serves to
strengthen the Fund significantly.
Of note is that the iterations with the highest expenditures (e.g., the
tenth percentile iteration) result in negative Fund balances even with the
simulated revenue policy. The claims in these iterations cause the balance to
drop dramatically. However, increases in the tax rate enable the Fund to
recover (i.e., return to a positive balance) in cases that were not possible
with the existing revenue policy. In four of the 29 iterations the tax rate
had to be increased an average of nearly $70 per dry weight ton (1982 dollars)
during the period examined.
Simulation 2. Current PCLTF Statute with Partial Ban on Land
Disposal. In the case of a partial ban on land disposal, the simulated
revenue policy again significantly enhances Fund performance. This occurs
despite the fact that the tax base is significantly smaller. As in simulation
1, the improved performance is due in large measure to the much larger
interest-earning balance that is built up in the early years of the Model
run. Also important is the fact that the tax rate is adjusted for inflation.
Further, in the later years of the run, the inflation-adjusted ceiling will be
much higher than the unadjusted $200 million ceiling under the current
statute. Consequently, the tax is collected more frequently and at higher
rates. These policies thus offset the effect of a significant reduction in
the size of the tax base (i.e., the volume of wastes disposed) brought about
by the limitation on land disposal.
Simulation 3. Coverage of Non-Routine Monitoring and Response Costs
Following Qualification. Making the PCLTF responsible for non-routine
monitoring and response costs (i.e., corrective action) during a facility's
post-closure period significantly increases total Fund expenditures. Rather
than a year 100 mean balance of -$103 billion (i.e., the result of the current
revenue policy), the simulated policy produces a mean final balance of $6
billion. Again, the critical difference involves the higher ceiling. In year
23, the mean ceiling exceeds $1 billion. While it drops below $500 million by
year 48, it climbs to over $23 billion by year 91. During the same period,
the ceiling under the current revenue policy stays fixed at $200 million.
This difference, coupled with inflation- and reserve-based increases to the
tax rate, leads to substantially higher revenue collection.
Simulation 4. Elimination of Third-Party Claims from PCLTF Coverage.
Because third-party claims represent a large fraction of PCLTF spending, Fund
expenditures are much lower in this simulation. Consequently, the improvement
in the Fund due to the use of the alternative revenue policy is much smaller
in this case-than with the first three simulations. Some improvement in Fund
solvency does occur, however, with a higher mean ending balance ($38.6 billion
vs. $8.9 billion) and a smaller chance of a negative balance (i.e., it
eliminates the 10 percent chance of a negative balance in the last nine years
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5-9
of the Model run, which arises under the current revenue policy as shown in
Exhibit 5-1). This improvement in Fund solvency is again due to the effect of
the higher Fund ceiling. After reaching the ceiling in year 12, the balance
does not drop below the ceiling again in any iteration for the remainder of
the time horizon. This higher balance, along with the interest it earns,
serves to keep'the balance from becoming negative.
5.4.2 Break-Even Tax Rate Analysis Results
The results of the break-even tax rate analysis are presented in Exhibit
5-2. As noted previously, these results are not related to the revenue policy
described in Section 5.3; instead, they are rates calculated to produce a year
100 fund balance of $10.7 billion (about $200 million in 1982 dollars). As
such, the tax is assumed to be collected every year and, other than an
inflation adjustment for the indexed rate, is not adjusted.
As expected, if the rate is kept constant in nominal terms, it must start
out at a higher level than an indexed rate in order to achieve the target
balance. Of note is that the mean and median rates for the constant tax rate
in simulations 1, 2 and 3 are above the current $2.13 rate. Tax rates kept
constant in real terms may be much lower. An indexed tax rate of $5.00 per
dry-weight tori would provide sufficient revenues for the simulations examined
here. ;
5.5 CONCLUDING NOTE
The revenue analysis presented here suggests that numerous fund management
techniques can ;be employed to ensure a target balance. Given the policies and
other assumptions upon which the Fund simulations are based, the effects of
the alternative Fund management practices are demonstrated to lead to solvent
Funds.
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5-10
EXHIBIT 5-2
SUMMARY STATISTICS FOR BREAK-EVEN TAX RATES1
(Dollars per ton)
Simulation
1. Current PCLTF Statute
• Indexed tax rate
• Constant tax rate
2. Current PCLTF Statute
with Partial Ban on
Land Disposal
• Indexed tax rate
• Constant tax rate
3. Coverage of Non-
Routine Monitoring and
Response Costs Follow-
ing Qualification
• Indexed tax rate
• Constant tax rate
4. Elimination of Third-
Party Claims
* Indexed tax rate
• Constant tax rate
Tenth Ninetieth
Percentile Mean (Std. Error) Median Percentile
.39
.99
.45
1.07
.49
1.26
.22
.56
1.43
3.65
2.07
4.84
1.56
3.97
.29
.75
(.23)
(.60)
(-30)
(-70)
(.24)
(-62)
(.02)
(.04)
1.04
2.65
1.60
3.74
1.11
2.84
.26
.66
3.04
7.76
4.80
11.22
3.14
8.02
.44
1.14
1 The break-even tax rate is the year 1 rate which results in sufficient
tax revenues being collected so that total Fund revenues minus the Fund
expenditures equal the target balance in the final year of the simulation
(which is $200 million in 1982 dollars).
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CHAPTER 6
MAJOR LIMITATIONS AND NEXT STEPS
This chapter discusses the major limitations in the current versions of
the PCLTF Simulation Model and the Release Simulation Model. In doing so,
this discussion illuminates those aspects of the analysis about which
potential users of the results should be particularly aware. Although many of
the limitations summarized here are discussed separately in Chapter 2 and
Appendices A and B, they are presented together in this chapter to provide an
understanding of the overall limitations of the entire analysis.
The first section highlights how key assumptions about EPA policies, the
behavior of the regulated community, and the behavior of potential claimants
affect estimates of PCLTF adequacy. Changes in these assumptions can have a
significant influence on adequacy estimates. Section 6,2 presents the
limitations of the general modeling approach. Limitations due to assumptions
made to simplify the analysis and the quality of data used are discussed in
relation to the PCLTF Simulation Model (section 6.3), followed by details
specific to the Release Simulation Model (section 6.4). , Finally, section 6.5
briefly enumerates some next steps in the development and use of the PCLTF
Simulation Model.
6.1 KEY ASSUMPTIONS AFFECTING ESTIMATES OF PCLTF ADEQUACY
Estimates of PCLTF adequacy reported in Chapters 4 and 5 are contingent on
several key assumptions. This section'highlights major simplifying
assumptions and identifies whether they cause adequacy to increase or
decrease. (An increase in adequacy means higher Fund balances over time, a
decrease means lower Fund balances.) Appendix A provides detailed
descriptions of; each assumption, as noted below.
Assumptions!may increase or decrease estimates of PCLTF adequacy by
affecting estimates of:
• revenues;
• facility qualification;
• the need to take corrective action; and
• claims.
Exhibit 6-1 displays the key assumptions influencing each of these four
areas. Regarding revenue estimates, full compliance with the tax and tax
collection from injection well and land treatment wastes increase total
revenues. Modifying these assumptions would reduce tax revenues so that the
Fund would take longer to reach a $200 million balance, and would not recover
as quickly from large expenditures. (Tax revenues are described more fully on
pages A-116 to A-119.)
By omitting cost recovery revenues from potentially liable parties (e.g.,
generators) the revenues may be underestimated. It may be different for the
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EXHIBIT 6-1
THE EFFECT OF KEY ASSUMPTIONS ON FOND ADEQUACY ESTIMATES
Revenue
Estimates
Facility
Qua!iTreat ion
Assumptions Which Increase Estimates
of PCLTF Adequacy
Full tax compliance assumed
Tax paid on portion or injection well
and land treatment wastes
Need to Take
Response Actions
Cla ims
Full five-year monitoring period
assumed
Any release prior to the end of the
five-year monitoring period causes
disquaI Ification
Actions fully effective if completed
No releases from injection wells or
land treatment facilities
Assumptions Which Decrease Estimates
of PCLTF Adequacy
No revenues from cost recovery
No disqualifications due
RCRA permit requirements
to non compliance with
ON
j Third-party claims are not brought in
response to on-site releases
Personal injury claims exclude puni-
tive damages, pain and suffering
Natural resource damage estimates are
restricted to restoration of small
areas of contaminated surface water,
value of lost recreation, commercial
value of fish kills.
Corrective action always required in response
to releases at landfill and surface impoundment
facilities (no alternative concentration
Iimits)
The population making personal injury claims is
assumed to be 75 percent of all individuals
with the types of diseases and conditions which
may result from exposure to hazardous
substances
The claims for medical costs are not reduced
amounts which may be covered by private
insurance or other government programs.
by
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6-3
Fund to recover from liable parties, but if cost recoveries were significant
they might stabilize the Fund against the shock of large expenditures.
Facility qualification is restricted by assuming that any release detected
prior to the end of the full five year monitoring period disqualifies a
facility from coverage. This assumption limits coverage, and consequently
increases estimates of PCLTF adequacy. Alternatively, the potential for
disqualification on the basis of non-compliance with RCRA permit requirements
is not modeled. This assumption moves in the opposite direction. (Facility
qualification requirements are discussed more fully on pages A-139 to A-141.)
The need to take corrective actions is caused by the detection of
releases. The .number of releases detected is reduced by (1) the assumption
that on-site cleanup actions which are completed are fully effective in
preventing off-site releases from occurring; and (2) releases are not modeled
at injection well and land treatment processes, and consequently response
actions are not taken. Balancing against these reductions in actions required
is the assumption that all detected releases lead to a requirement to take
action. It is assumed that alternate concentration limits are not set by the
Regional Administrators. (The factors leading to the simulation of actions
are discussed more fully on pages A-124 to A-133.)
The estimates of claims are one of the most uncertain estimates in the
Model. Claims'estimates are reduced by the assumptions that: (1) claims are
not brought in response to on-site releases; (2) personal injury claims are
limited to lost wages and medical costs, excluding punitive damages, pain and
suffering, and .death benefits; and (3) natural resource claims are restricted
to small areas with specific impacts. Alternatively, claims estimates are
increased by the assumption that the background incidence of diseases and
conditions potentially related to exposures to hazardous wastes forms the pool
of potential claimants. It is assumed that 75 percent of this background rate
makes full claims on the fund. Additionally, it is assumed that the claims
are not reduced by amounts paid for by private insurance or other government
programs. (Claims are discussed more fully on pages A-81 to A-102.)
Significantly altering the above assumptions can change the estimates of
PCLTF adequacy by substantial amounts .in the out years. Consequently, in
interpreting the Model results, it is important to recognize the roles played
by these and other model assumptions. The next section describes the overall
limitations to .the modeling approach.
6.2 GENERAL MODELING LIMITATIONS
The PCLTF Simulation Model is a stochastic simulation model, meaning that
key aspects of the Model are based on probability principles. This modeling
approach is limited to providing distributions of expected outcomes (e.g.,
Fund balance) which are described by various statistics (mean, median,
standard error). The model should not be used to generate point estimates of
individual outcomes.
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6-4
The stochastic simulation approach is particularly well suited for
describing the characteristics of populations; in this case, the population is
hazardous waste land disposal facilities. As a whole, the population of
facilities can be described by simulating individual facility characteristics
based on the expected distribution of those characteristics throughout the
population (the expected distribution may be based on a sampling from the
entire population). Although the population as a whole may be well described
in this manner, the characteristics of any individual facility may not be.
Consequently, the Model is not meant to be used to evaluate the anticipated
performance of individual facilities. In order to examine an individual
facility, detailed data about that facility would have to be collected and put
into a form that the Model could use.
A final caveat regarding the overall Model is that its primary output, the
Fund balance over time, cannot be verified empirically. The overall
performance of the Model cannot be calibrated by comparing the Model output to
observed values. The verification of the Model is therefore limited to the
verification of its individual components.. As a whole, the Model results can
be evaluated only in terms of their reasonableness, intuitive plausibility,
and ability to be reproduced.
6.3 PCLTF SIMULATION MODEL
As with all models, the PCLTF Simulation Model is limited by the
simplifying assumptions and data underlying its development and use. The most
important simplifying assumptions in the Model relate to the presumption of
independence among various components of the analysis. For example, one
portion of the Model simulates the occurrence of releases, and a separate
portion estimates the response costs and third-party claims which arise as a
result of the simulated releases. The potentially limiting aspect of this
procedure is that the first portion (simulation of releases) is performed
independently of the second portion (estimation of response costs and third-
party claims). Although the implicit assumption of independence may be valid
in the majority of cases, it may be inappropriate for certain situations.
Specifically, if potential third-party claims would be very large if a release
occurred at a particular facility (e.g., if the only available drinking water
source for a large population were at risk), special care may be taken by the
owner/operator, or state or federal authorities to ensure that no release
occurred. In other words, the magnitude of a release's potential harm could
feed back into the likelihood of whether the release occurs. This feedback is
not included in the Model.
The importance of not including this particular feedback mechanism depends
on two factors:
• whether harms of sufficient magnitude to initiate the
heightened concern of owners/operators, state and
federal authorities are simulated; and
• whether this heightened concern can successfully
influence the likelihood of a release occurring.
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6-5
A small number, of very large third-party claims are simulated in the Model (on
average, only one claim in excess of $500 million (1982 dollars) is simulated
over the next 100 years). The potential for claims of this magnitude may be
sufficient to create the heightened concern needed to cause this feedback
mechanism. However, the extent to which this heightened concern can succeed
in reducing the chance of a release is unclear, especially for existing
facilities which may be well on the way to having a costly release. Influence
over future very costly releases is much more easily gained at new facilities
because siting in certain sensitive areas can be prohibited. To the extent
that very costly releases will be avoided at existing and new facilities due
to individuals undertaking special measures, the Model may overestimate these
releases.
One way to guard against this potential bias in using the Model results is
not to utilize the estimate of the minimum Fund balance. This estimate (and
its associated estimate of the maximum Fund expenditure) is more susceptible
to this bias than the other estimates of Fund performance. Consequently, less
importance should be placed on the estimated minimum Fund balance than would
otherwise be appropriate, and greater reliance on the tenth percentile
estimate is warranted.
Other similar simplifying assumptions of particular significance include:
• Availability of State Funding Mechanisms. The availability
of state funding is not influenced in the Model by the
availability of PCLTF or other federal funds. One might
anticipate that a reduced federal role could prompt increased
state involvement. For example, if PCLTF coverage were very
broad, states may play a smaller role than if PCLTF coverage were
strictly limited. Although the Model does not automatically
incorporate this potential relationship, the implications of this
feedback can be modeled by using alternative assumptions
regarding the availability of state funds. Through successive
runs of*the Model, one could estimate the increase in unfunded
costs due to a restriction of PCLTF coverage. Based on this
estimate, one could hypothesize the extent to which states might
pick up this slack and the user could then develop an alternative
scenario of state funding. This alternative state coverage
scenario could then be used to assess the overall implications of
a restriction in PCLTF.coverage.
• Actions Taken and Availability of Funds. The choice of
actions"taken in response to releases is driven by a set of
action policies reflecting current RCRA regulations. The choice
and timing of actions are influenced by whether funds are
available to take the action. If no funding source is available,
the action is presumed not to be taken. If a funding source is
available, the action is presumed to be taken as long as the
funding source remains available. This procedure provides a
rough indication of whether and when actions will be taken. Not
included in the analysis is an opportunity for the funding
sources to set priorities among competing demands for funds. All
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6-6
required actions are assumed to be taken promptly when funding is
available. However, the PCLTF, state funds, and the Superfund
may set priorities for actions so that some actions of lesser
importance are delayed. These delays may smooth out peaks in
funding requirements without substantially affecting total costs
over time. Also, if due to a lack of funds, the PCLTF or state
funds undertook less expensive actions than those required of
owner/operators by RCRA regulation, then Model estimates of PCLTF
and state expenditures may be overestimated.1
Changes in Legal Regimes Over Time. Legal regimes describing
the legal validity of third-party claims (based on state
statutory and common law and on federal common law) do not evolve
over time in the Model. Clearly, changes may take place in the
ability of claimants to establish legal liabilities (for example,
CERCLA established liability under federal law for natural
resource damage). The rate at which legal regimes change over
time may be influenced by the extent to which harmed parties are
unable to recover for claims. The importance of this potential
third-party influence on the evolution of legal regimes is
uncertain. The Model simulates that approximately 15 to 30
percent of third-party claims may not be legally valid under
current legal regimes. It is clear, however, that current
proposals for victim compensation legislation may have a
significant influence on the ability of harmed parties to
recover. In fact, the Model may be a useful tool for evaluating
the implications of these various victim compensation
alternatives.
Generator Liability. As described in Chapter 2, generators
are not included as a potential funding source for costs arising
at land disposal facilities. The inclusion of generators could
not only influence the manner in which costs are distributed
among the funding sources, but could also influence the total
quantity of generated waste that is placed in land disposal
facilities. Generators may prefer other waste management options
with less potential future liability.
Owner/Operator Behavior. Potentially important aspects of
owner/operator (0/0) behavior are not-modeled. Most important,
the ability of 0/Os to isolate themselves from potential
1 The likelihood of the PCLTF or state funds having a less stringent set
of action policies than those imposed on owner/operators may be very small.
In particular, the RCRA action requirements are designed to protect the
environment and human health and welfare. Altering these requirements for
government-sponsored responses may be considered to be inconsistent. The
Model may be used to examine the implications of alternative action policies,
but as the Model is currently configured, these policies must apply to all
parties.
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6-7
liabilities is not assessed.2 Because 0/Os play a large role in
the coverage of costs, their behavior could have significant
implications.
In addition to these simplifying assumptions, the Model is limited by the
data that were used to estimate its parameters and relationships. EPA has
recently completed several major data collection efforts which represent the
best available! information on the existing population of land disposal
facilities.' The analyses presented in this report incorporate these data as
well as supplementary information from other sources. However, several
important data limitations remain, in part because several estimates of the
distribution of facility characteristics throughout the facility population
are based on small samples (e.g., fewer than 50 observations). These data
limitations include:
• Facility Characteristics. The estimates of facility
characteristics most limited by data availability and quality are
the facility characteristics affecting costs.3 The important
facility characteristics affecting the cost estimates relate to
descriptions of the physical characteristics of the site (e.g.,
depth to groundwater) and to the factors affecting the potential
for third-party damages. These characteristics are modeled using
representative values and ranges reflecting their expected
distributions in the population. However, several of the
estimated distributions are based on particularly scanty data,
including: use of surface and groundwater near facilities,
potential for natural resource damage, and depth to the uppermost
aquifer.
• Response Actions. Data on response actions are limited in
two respects. First, while the best available cost estimates do
incorporate uncertainty about hydrogeologic conditions and plume
dimensions, only plume dimensions are explicitly simulated by the
PCLTF Model.* Consequently, hydrogeologic conditions may vary
substantially from the conditions upon which the costs estimates
are based. In individual situations, actual costs may be higher
or lower than those simulated. It is currently believed that
fluid removal and treatment will be required for extended periods
of time in order to meet the current corrective action
2 The bankruptcy of owner/operators due to large costs is assessed.
However, actions undertaken specifically to avoid liability are not examined.
3 Estimates of releases are also limited by data availability and
quality. These limitations are discussed separately in the next section.
* Geraghty & Miller, Inc., "Stochastic Model of Corrective Action Costs
at Hazardous Waste Management Facilities," January 1984.
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6-8
regulation.5 Consequently, once begun, fluid removal is
assumed to last the duration of the Model run. However, after
the initial cleanup is completed, annual O&M costs may be
expected to drop by about 10 percent. Because there is
anticipated to be considerable site-specific variability in the
timing of this O&M cost change, it is not modeled at this time.
Appendix A describes the PCLTF Simulation Model in detail, including all
the data sources and assumptions incorporated into the Model. The reader is
encouraged to refer to this appendix for more detail on the specific
limitations of particular components of the Model.
6,4 RELEASE SIMULATION MODEL
The simulation of releases is a key aspect of the overall assessment of
the adequacy of the PCLTF. The Release Simulation Model developed by Battelle
Pacific Northwest Laboratories utilizes standard methods for estimating the
transport of groundwater contamination. These methods have been adapted for
the purposes of supplying the PCLTF Simulation Model with matrices describing
the potential for releases at land disposal facilities. Many of the
limitations of this model are common to all models of groundwater
contamination. The limitations resulting from the simplifying assumptions
used and data availability are discussed separately.
6.4.1 Release Simulation Simplifying Assumptions
Important simplifying assumptions can be divided into three parts:
• weather simulation;
• water balance calculation; and
• contaminant transport.
The modeling process begins with the simulation of weather for a given
site. Since we do not understand the underlying relations sufficiently to
predict long-term weather trends in a deterministic manner, weather is
considered a random event and is simulated using, distributions of monthly
temperature and precipitation. Because the distributions of monthly
temperature are based on 40 years of historical data for each month, the
simulated weather will incorporate some features reflecting the
interdependence of conditions between successive months. However, no explicit
estimate in the inter-month interdependence is included. Consequently, one
month is not influenced by the simulated weather for the preceding month.
In addition, weather is simulated on a monthly basis (as opposed to daily
or hourly). As a consequence, singular extreme events such as multimonth
droughts, excessive rainy periods, and significant storm events have an
extremely low probability in the simulation.
5 40 CFR 264, subpart F, Ground-Water Protection: Corrective Action
Program.
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Weather data are taken from the records of the 300 primary weather
stations in the U.S. By virtue of the spread of waste disposal facilities
across the nation, weather simulations at some facilities rely on data from
relatively distant weather stations. Hence, differences arising from local
topography and other geographic features will not be evident.
The impact of the limitations on weather simulation could be significant
because this component defines the availability of water to form leachate and
subsequently migrate outwards. However, because errors could lie in either
direction (excessive or insufficient temperature and precipitation), the
average estimates across all facilities are believed to be unbiased.
The water balance calculations are made to determine how much
infiltration will occur and what portion of it will emerge as leachate. The
relations involved are of a simple bookkeeping nature. In each period,
incremental infiltration is calculated and added. Precipitation defines the
total amount of water available. A 'portion will run off, a portion will
evaporate and transpire (from plants), and a portion will infiltrate into the
top layer. When the top layer becomes saturated, moisture can move on into
the next layer", and so on. If a clay layer is involved, the rate of water
movement is determined using a hydraulic conductivity curve.
The water balance approach, like the weather simulation, deals with each
.month as a discrete time step. As such, monthly totals are dealt with rather
than individual storm events. This monthly aggregation may overestimate the
rate of releases.
The water balance approach implicitly assumes that releases stem from the
continual movement of waters into and out of the disposal site. It neglects
to consider acute events such as flooding, earthquakes, and human intrusion.
Although the importance of flooding and earthquakes is expected to be small,
experience suggests that human intrusion may be a significant cause of
releases. Given sufficient time, virtually all sites will be disrupted in one
fashion or another. The timing and extent of that disruption cannot be
predicted. Because human intrusion is not modeled, the current water balance
approach underestimates potential failures in the long run.
The third set of simplifying assumptions relates to the way in which the
transport of contaminants is modeled. This portion of the Model predicts the
movement of the key contaminants in the aquifer once the leachate has entered
the saturated zone. A simple three-dimensional transport model is used which
considers dispersion, dilution, and soil adsorption. Limiting factors include:
• There is no way to predict where within a site the leachate
will breach the liner. As a consequence, the Model assumes
failure across the entire site. This simplification creates a
wider plume than would be anticipated.
• Of necessity, only representative constituents are considered
and interactions among constituents are not accounted for.
Synergistic and antagonistic effects influencing toxicity and the
rate of transport are not modeled.
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• The Model does not address multiple-aquifer contamination.
Hence, for those sites where interaquifer communication exists,
the model will underestimate both dilution and the extent of the
area affected by a release.
• The Model does not consider the possibility of changes in water
table elevation which could increase or decrease impacts
depending on the direction of change.
• The Model does not consider degradation or chemical
interactions of constituents which could reduce leachate levels
or create new, more toxic species.
While each of the above considerations may have significant impacts on a
specific site, it is difficult to predict their overall impact on the Model
output. If any bias exists, it would appear to be that the Model
underestimates the potential for releases and overestimates the time it takes
for releases to occur. This observation is based on the inability of the
model to cover such features as extreme weather conditions, human intrusion,
injection well facilities, and interaquifer transfers.
6.4.2 Release Simulation Data Limitations
As with all modeling efforts, the quality, of the output from the Release
Simulation Model is driven by the quality of the input data used. The major
limitations of the key data elements are as follows:
* Performance of Synthetic Membrane Caps and Liners. There is
a paucity of data on the expected failure rates of synthetic
membranes. As a consequence, the values used have little basis
in empirical experience. Sensitivity analysis indicates that the
assumptions regarding the performance of synthetic membranes can
influence the timing of releases, but have little impact on the
overall potential for releases to occur.
• Hydrogeologic Data. The National Data Base developed for
the Release Simulation Model provides data for 7.5 minute grids
(cells 7.5 minutes longitude by 7.5 minutes latitude throughout
the U.S.). These data have not been explicitly acquired for
every grid cell, although numerous data points were analyzed (see
Appendix B). Where data are not available for a cell, values
based on averages across larger regions are used. Limitations of
the data on individual hydrogeologic parameters include:
-- Seepage Velocity. Aquifer seepage velocity was determined
for the major groundwater regions in the country based on the
analysis of general conditions described in published
reports. In many cases, average values are used for large
areas, meaning that the full range of field conditions is not
reflected.
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-- Runoff Coefficients. Runoff coefficients were calculated on
the basis of hydrologic soil groups and assumed rainfall
events in the range of 0-8 inches. Because the soil data are
on a large scale, they cannot reflect local patterns in soil
types. This could cause significant inaccuracies at
individual sites, but no bias is anticipated in the aggregate.
-- Depth to Groundwater. These data are based on 1,200 values
reported in the 1978 EPA surface impoundment survey.6 If
no data point is in the 7.5 minute cell of interest, values
are drawn from nearby cells and averaged.
Field Capacity of the Soil. These data are based on field
capacity data for soils and the Soil Conservation Service
soil map. The large scale of the mapped information prevents
consideration of local variations. In addition, the data are
for surface soils as opposed to deeper or imported soils.
The net effect of these factors is likely to be small in the
aggregate. If anything, they may overestimate field capacity
because sites are likely to be designed not to hold water,
and therefore may have less capacity than local native soils.
Total Dissolved Solids. Data on water quality were taken
from the 1978 EPA surface impoundment survey.7 These data
may overestimate water qualify because facility siting
probably favors areas with low water quality (i.e., a high
concentration of total dissolved solids). As a consequence,
the Model may underestimate the amount of constituents
required to show a significant increase in indicator
parameters and will, therefore, predict a Type 1 release too
early.
* Waste Data. This data base contains a listing of all major
toxic constituents and the properties of interest for those
constituents. The major limitation on these data are the
potential omission of trace or nontoxic constituents with
important characteristics and the failure to handle interactions
between constituents and between wastes.
Of the above data limitations, most are anticipated to cancel each other
in the aggregate. To the extent that the results are biased, it appears that
releases may be underestimated.
6 U.S. Environmental Protection Agency, "Surface Impoundments and Their
Effects on Groundwater Quality in the United States -- A Preliminary Survey,"
(510/9-78-005), June 1978.
7 Ibid. '
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6.5 NEXT STEPS
As indicated in Chapter 1 of this report, the analyses presented here
are the result of the third stage of a multi-stage effort undertaken to
assess the adequacy of the PCLTF. The current versions of the models
presented in this report include modifications that were made based on
comments received on previous versions. These modifications, as well as
the incorporation of new data collected by EPA, have enhanced the models
substantially, increasing their validity. Important next steps in the
continued development and use of the models include:
• Validation and Review. Although the estimates of the PCLTF
balance cannot be empirically verified, the individual components
of the Model estimates should continue to be reviewed and
validated. For example, the Model results imply that
approximately one-half of existing land disposal facilities may
currently have groundwater contamination at detectable levels.
To the extent that this estimate can be calibrated with field
observations of groundwater quality, the Model's estimates of
releases can be calibrated.
• New Data. The model should incorporate any new or revised
data as they become available. As EPA collects data on
facility-level characteristics at existing facilities, the
distributions reflecting the characteristics should be revised.
More important, data on the uses of water located near facilities
and on the potentially-exposed population should be updated.
• Model Enhancements. Several enhancements of the PCLTF
Simulation Model may be particularly useful at this time.
-- Generators could be added as a potential funding source.
This change would require an analysis of the likely
ability to recover costs from generators, or at least the
identification of the various possible scenarios of
generator liability. The addition of -generators would
increase the scope of the Model, increasing its value as
a tool for assessing various RCRA issues.
Because owner/operators are simulated to cover such a
large share of costs, an assessment of their ability to
isolate themselves from these costs is warranted. The
Model may currently overestimate the frequency with which
owner/operators will cover costs, thereby underestimating
the magnitude of uncovered costs.
-- A more explicit risk assessment methodology could be
implemented to determine possible personal injury damages.
Explicit feedback effects could be implemented in the
model. For example, the relationship of liability and
business failure could be implemented so that
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owners/operators could be simulated to practice strategic
behavior in the face of large potential liability costs.
In general, behavioral response feedbacks of this type
can be analyzed in several places within the model.
Because the amount of wastes disposed of via land
: disposal may decrease significantly over the next 10
years, the closure of facilities due to a decrease in
demand should be modeled. For decreasing waste
scenarios, the model may overestimate the number of
operating facilities during some time periods.
-- . The estimation of the leachate plume size is currently
based on the facility size (in terms of its surface area)
and on the distance to the nearest potable well or
natural body of water. Additional information such as
the amount of leachate released from the facility may
improve estimates of the plume size.
• Sensitivity Analysis and Other RCRA Issues. The Model
should continue to be used through the performance of various
sensitivity analyses. These analyses will be useful for
examining not only the PCLTF, but also a variety of other RCRA
issues. For example, one can examine the implications of
alternative monitoring requirements that would increase the
ability1 to detect releases where they occur. Alternatively, the
role that banning certain wastes from landfills could play in
reducing future releases can be assessed. Also, the cost
implications of a policy that would not require a corrective
action if no threat were posed to off-site groundwater could be
examined. These and other analyses could provide useful insight
into various RCRA issues.
In sum, the*present stage of PCLTF Model development is sufficiently
advanced to yield, we believe, useful estimates of the Fund balance over
time. The enhancements discussed here can increase the robustness of the
Model and its usefulness for examining a variety of RCRA issues.
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