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August 7, 2002 TMDL/JS 1
A Stakeholder Process for Formally Evaluating TMDL
RecommendationsDominguez Channel
Lawrence Livermore National Laboratory
With Assistance From
Dominguez Channel Stakeholders, EPA, and the Regional Water Board
August 7, 2002 TMDL/JS 2
plumes
plume
TMDL allocation common data requirements are hydrological and land use data
residential
agriculture
industrial
parks
August 7, 2002 TMDL/JS 3
Top level concepts for TMDL decision aid components
TMDL allocation TMDL allocation options options
Model selection Model selection and/or data and/or data selection selection
Trading optionsTrading options
Contaminant Contaminant concentrationsconcentrations
Cost/scheduleCost/schedule
Stakeholder inputStakeholder input Stakeholder inputStakeholder input
Diverse ConcernsDiverse Concerns• HealthHealth• EnvironmentEnvironment• Private industry Private industry • Land UseLand Use• Regulatory bodiesRegulatory bodies• Congress Congress • Interest groupsInterest groups• Local economyLocal economy• Indirect costIndirect cost
Decisions/policies
Scenariospecs
Metrics/attributes
Value model
Decision/policy Decision/policy evaluationevaluation
Constraints
August 7, 2002 TMDL/JS 4
Decisions/policies
Numerous options for data or modeling
Constraints and scenario specification
TMDL,Time horizons
Uncertainties: nature of site, future monitoring results, delays, government decisions, revised allocation performance
Metrics/attributes
Multiple concerns and stakeholders
Measures for how well diverse concerns are addressed
Decision/policy evaluation
Value tradeoffs
Overall cost
A decision aid helps to structure each of the key parts and logically put them together
A decision aid helps address the important features systematically
August 7, 2002 TMDL/JS 5
Metrics/attributes - desirable properties
comprehensive: cover all important aspects
non-redundant: do not double count
operational: can be estimated for alternative actions are meaningful to decision makers for tradeoffs
decomposable: simplify both consequence and value modeling (e.g., satisfy helpful independence assumptions)
minimal number: must show meaningful differences between alternative actions
Types of attribute scales:
- natural scales: commonly used such as time or $$
- constructed scales: discrete levels each associated with a well-defined description of conditions
(not meaningful: arbitrary 0-10 scales that are not defined)
Attributes formally measure the degree to which concerns are addressed by decisions
Diverse ConcernsDiverse Concerns•HealthHealth•EnvironmentEnvironment•Private Industry Private Industry •Land UseLand Use•Regulatory bodiesRegulatory bodies•Congress Congress •Interest GroupsInterest Groups•Local economyLocal economy•Indirect CostIndirect Cost
Diverse ConcernsDiverse Concerns•HealthHealth•EnvironmentEnvironment•Private Industry Private Industry •Land UseLand Use•Regulatory bodiesRegulatory bodies•Congress Congress •Interest GroupsInterest Groups•Local economyLocal economy•Indirect CostIndirect Cost
August 7, 2002 TMDL/JS 6
Health
Interest groups
Regulators
Environment
Land use(Socioeconomic)
Congress/ Local Governments
Indirect cost
Time to meet Federal TMDL for COC’s*
Time to meet State/Local TMDL for COC’s
Cumulative time by which milestoneshave slipped for regulatory agency
Loss of jobs; increased land values (may be handled chiefly as a constraint)
Attributes - Illustrative straw-man set
Land use (constructed scales)
Interest group (constructed scale)
Perception of effectiveness of regulations (constructed scales)
*COC = contaminant of concern
Private industry Competitiveness (constructed scales)
August 7, 2002 TMDL/JS 7
Interest groups: constructed scale - public attitudes
1) remedy destroys contaminant (e.g., bio-degradation) and does not allow future releases2) remedy removes contaminant from one medium to another3) monitored natural attenuation (and no further contamination or dispersion)4) pollution reduction, but either lack of information to clearly identify source(s), or lack of proven technology to prevent further source contamination5) pollution reduction, but lacking information to identify source(s) and proven technology to prevent further source contamination6) no cleanup or pollution prevention technology but only institutional controls on exposure
August 7, 2002 TMDL/JS 8
Scenario specifications model attribute levels given decisions
Simple strawman illustration notions
Interest group wants specific area excluded from trading
Businesses threaten to relocate if cost excessive
Stakeholder inputStakeholder input Stakeholder inputStakeholder input
August 7, 2002 TMDL/JS 9
Scenario specifications model attribute levels given decisions
Simple strawman sub-model notions: impact of trading
Ability to •improve clean up rate
•meet more stakeholder concerns
•produce more allocation options
August 7, 2002 TMDL/JS 10
Value model
Decision/policy Decision/policy evaluationevaluation
A value model incorporates preference tradeoffs and attitudes to compare alternative policies/actions
• preferences for levels of individual attributes • tradeoffs among attributes for a watershed
A value model provides a summary number (utility or value) for each alternative consistent with the preference information, and consequence estimates for that alternative.
August 7, 2002 TMDL/JS 11
Value model
Decision/policy Decision/policy evaluationevaluation
A value model incorporates preference tradeoffs and attitudes risk to compare alternative policies/actions
Multiattribute utility/value function theory provides defensible assumptions and practical functional forms for quantifying values.
U(x1, x2,...,xn) = wivi(xi) (additive form)
U(x1, x2,...,xn) = [ (1+Kwivi(xi))-1]/K (multiplicative form)
where:
U is the overall summary (utility/value) number;
xi are the levels for individual attributes;
vi are individual attribute utility/value functions (scaled between 0 and 1);
wi are scaling constants or weights reflecting the relative importance of the different attributes (tradeoffs) ranging from their worst to best levels (scaled between 0
and 1, with wi = 1 for the additive form);
K is a normalizing constant (computable by first solving for the variables Ci = Kwi and then letting K = [ (1+ Ci)-1] for the multiplicative form.