Prioritization using
Logic Models and
MIRA
October 17. 2007
Instituto Nacional de Ecologia
Mexico City, Mexico
Part I: Logic Models
Connecting Program Activities to
Environmental Outcomes
What is a Logic Model?
Tool to help understand how program
activities affect environmental outcomes.
Kellogg Foundation template to start.
Foundation Home Page:
http://www.wkkf.org/Default.aspx?LanguageID=
0
Logic Model Guidance Document:
http://www.wkkf.org/Pubs/Tools/Evaluation/Pub
3669.pdf
Modification for environmental programs.
Logic Model Template
Baseline
What is the
condition of
the things
we care
about?
Resources
In order to
accomplish
our set of
activities we
will need
the
following:
Stressors
In order of
importance
what are
the
stressors
and where
are they
most
prevalent?
These will
be used to
target
activities.
Activities
In order to
address our
problems or
asset we
will
accomplish
the
following
activities.
Outputs
We expect
that once
accomplish
ed these
activities will
produce the
following
evidence or
service
delivery.
(bold = perf
stds)
Short and
Long term
outcomes
We expect
that if
accomplish
ed the se
activities will
lead to the
following
changes in
1-3 then 4-6
years.
Impact
We expect
that if
accomplish
ed these
activities will
lead to the
following
changes in
7-10 years.
How to build a logic model
Brainstorm activities (“individual logic
model”)
Program or site activities
For each activity, ask:
Why do I do this activity?
What is(are) the intended outcome(s) of doing
that activity?
What is(are) the actual outcome(s)?
What is the impact (ultimate goal) of this
outcome?
How to build a logic model – cont’d.
Baseline = outcome/impacts
Need to measure the same thing at the
baseline as at the end.
Stressor – Distinguish between:
Pollutant stressors
E.g., population growth, vehicle emissions
Program stressors
E.g., conflicting statute, no regulatory authority
Different stressors compel different
activities/outcomes.
Logic Models good for:
 Linking activities to outcomes/impacts
Helps to identify dependent activities.
If linking site activities, are different outcomes
expected/desired from different sites?
 Describing indicators needed to measure
programs.
Defining indicators is necessary for program evaluation;
Not always easy.
 Learning about your programs.
Examine why you do your activities.
What Logic Models are missing:
No indicator data contained in LMs.
No way to prioritize program activities.
Use MIRA to get these…
Part II: MIRA
Analyzing Information for
Decision Making: Prioritizing
Environmental Outcomes and
Managing Risk
What’s involved in Decision analysis?
Criteria/Data
Science – exposure, fate/transport models,
other
Program implementation (logic models).
Social science – environmental justice, different
demographic impacts.
Values
Integrative, contextual approach for
decision analysis.
MIRA
Multi-criteria Integrated Resource
Assessment
MIRA Approach:
Multi-criteria
Transparent
Data driven; relative analysis
Iterative/learning-based
MIRA
Data Collection
Manager
Geostatistical
Indicators Module
PRIMARY LEVEL
SECONDARY LEVEL
THIRD LEVEL
Data Fit
Region III
Data Scatter
FOURTH LEVEL
Area Wide
Population weighted
Design Value weighted
Attn. Threshold weighted
Area Wide
Population weighted
Design Value weighted
Attn. Threshold weighted
Worst Outlier
Data Fit
1-Hr O3 Non-Attainment Areas
Ozone Air Quality
Data Scatter
Area Wide
Population weighted
Design Value weighted
Attn. Threshold weighted
Area Wide
Population weighted
Design Value weighted
Attn. Threshold weighted
Worst Outlier
Data Fit
1-Hr O3 Attainment Areas
Data Scatter
Area Wide
Population weighted
Design Value weighted
Attn. Threshold weighted
Area Wide
Population weighted
Design Value weighted
Attn. Threshold weighted
Worst Outlier
Data Fit
Class I Areas
Data Scatter
Worst Outlier
Personnel Impact
Costs
Trends Impact
Monitor Servicing Distance
Work Load
Area Wide
Population weighted
Design Value weighted
Attn. Threshold weighted
Area Wide
Population weighted
Design Value weighted
Attn. Threshold weighted
Programmatic and
Budget Decision
Analysis Module
Fate and
Transport
Models
What is MIRA designed to do?
Policy Development
Assist in multi-criteria analyses for the
development/implementation of policy.
Understanding alternatives
Improve understanding of the relationship
between the data and the decision alternatives.
Address stakeholder concerns
Provide an analytical framework for reflecting
stakeholder ideas (Inclusive)
Steps in the MIRA approach
 Determine the decision question.
 Brainstorm initial criteria.
 Gather data for those criteria.
 Construct the analytical hierarchy for the
decision question.
 Index data (expert input).
 Preference criteria (stakeholder value sets).
 Iterate; Learn.
Hazard Ranking System (HRS)
Example
Possible to use HRS score in different
ways with MIRA:
Option 1: Use HRS as a decision criterion.
Option 2: Use HRS criteria and allow for
flexibility for expert input and decision maker
judgment.
Appropriate when you don’t have or can’t get type of
data required by HRS; i.e., need to use surrogate
indicators.
Option 1: HRS as Criterion
Suppose you want to evaluate both the
condition of the region and program
effectiveness within the region to include:
Public health impacts
Ecological impacts
Balance condition with program
(in)effectiveness.
Possible to set up a decision hierarchy
something like this…
Option 1: Sample MIRA
Decision Hierarchy
Risk
Public Health
Source
Admin.
Condition
Habitat Condition
Ecosystem Health
HRS
?
Stressors
Admin.
Risk
Public Health
Program
Source
Admin.
Habitat Condition
Ecosystem Health
Stressors
Stressors
Option 1: Indicator Examples
 Condition
 HRS score
 Economic/social costs
 Ozone concentration, Nutrient load
 Cancer risk, Exposure
 Program
 # permits/regulations approved; % impaired streams
 % regulations that include evaluation of alternative control
technologies.
 Amount of time between submittal and approval
of…regulation/permit/plan.
 “x” type of Hazardous Waste implementation plan
producing change/improvement in “y” type of risk
parameter by “z” amount.
Option 1: How to use HRS with other
criteria
Need to consider the relative
environmental significance of HRS with
other criteria.
Expert discussion
What does HRS indicate? Is it a more decision
significant indicator than economic cost (for
example)?
If you believe no other criterion than HRS
needs to be considered, you don’t need MIRA.
Option 2: Using HRS criteria as the
analysis
Suppose you only want to consider
hazardous waste criteria as currently used
in calculating HRS…
OR: You are unable to get data required/
expected by HRS and must use surrogate
indicators…
Possible to set up decision hierarchy as
follows…
Likelihood of
Release
SGW
Waste
Characteristics
Observed
Release
Potential of
Release
Cancer
Toxicity/
Mobility
NonCancer
Chronic
NonCancer
Acute
Haz. Waste
Quantity
Source
Constituent
Nearest Indiv.
Waste Stream
HRS
Targets
Population
Resources
SSW
...
SS
...
SA
...
Sensitive
Ecosystems
Option 2: MIRA Hierarchy for Hazard Ranking
System (HRS)
HRS Calculation Example 1
 HRS: Likelihood of Release = greater of
observed release or potential to release
To replicate in MIRA: one of these criterion will have a
weight of zero in the calculation (Other = 1.0).
MIRA alternative (if not regulatory): weight these
criteria in any way that adds up to 1.0 (or 100%).
HRS Calculation Example 2
HRS Calculation Methodology
Pathway Score, S = (Likelihood of
Release x Waste Characteristics x
Targets)/82,500
Max values for LR = 550, Waste = 100,
Targets = 150.
Cont’d Example 2
 To replicate in MIRA:
Calculate relative weights for each of 3 factors.
E.g. LR weight = (550/82,500)/(550/82,500 +
100/82,500 + 150/82,500) = 0.691
(LR) x 0.691 x (waste) x 0.124 x (targets) x
0.185 (Fixed weights via HRS method)
Likelihood of Release is designed to be the
most important criterion in the HRS calculation
method (69% vs. 12% vs. 18%).
 With MIRA, you can change weights if desired
(and allowed by law).
HRS Calculation Example 3
 HRS =
2
2
S GW  S SW  S S  S A
2
2
4
 Max pathway score (S) = 100.
 HRS equation appears to weight all pathways
equally BUT actually weights the pathway
score that is highest most heavily (due to
squaring).
 In MIRA: possible to replicate weights via
above equation or use other weights.
Option 2: HRS Component analysis with
MIRA
 What’s different about using HRS criteria in MIRA vs.
just calculating HRS?
Allows for transparency in seeing relative importance
(weights) of all the criteria composing the HRS.
Possible to use additional criteria (economic/ social) if
desired.
Possible to use surrogate criteria if data required by
HRS is not available.
 If law requires HRS method, using MIRA is not an
option.
BUT could use MIRA to inform other decisions.
MIRA Approach
 Step 1: Determine the decision question.
 Step 2: Brainstorm initial criteria.
 Step 3: Construct the analytical hierarchy for the
decision question.
 Step 4: Address missing data.
 Step 5: Decide on decision’s unit of measure.
 Step 6: Index data (expert input).
 Step 7: Preference criteria (stakeholder value
sets).
 Step 8: Iterate; Learn.
Step 1: Formulating the Decision
Question
Decision makers/stakeholders formulate
the question that they want to answer and
the criteria they think they need to answer
it.
What are the problem set elements that
you are analyzing/ranking?
e.g. watersheds?, counties?, emission control
strategies?
Step 2: Brainstorm Initial Criteria
Are data available for these criteria?
Are data available on the scale that you
want?
States?, Counties?, watersheds?, stream segments?
Other?
If not:
Is another scale possible?
Can surrogate data be used?
Should this be identified for future data collection?
Step 3a: Construct the Decision
Hierarchy
Provides decision context.
Forces stakeholders to assess whether
they agree on the decision question that
they want to answer.
Step 3b: Methodological thinking for
constructing the hierarchy
Should each criterion currently organized
at each level of the hierarchy be directly
comparable?
E.g., Would you compare Arsenic in ground
water with Ozone air quality? OR would a
better comparison be Water (with groundwater
under it) with Air (with Ozone under it)?
Step 4: Determine which criteria
have no/missing data
Possible alternatives to no data
Health impact data – pollutant concentration – source
emissions – number of sources?
Data collected by volunteers/other organizations.
Using similar data (from another program, etc.).
Possible alternatives to missing data
Statistical analyses – e.g., multivariate analyses
Data collected by volunteers/other organizations.
Modeling.
Note about previously constructed
indicators
What do these indicators indicate?
Is this meaningful in your current analysis?
Can better indicators for your analysis be
constructed with currently available data?
Step 5: Deciding on the Decision’s
unit of measure
Depends on the decision question
What is the condition of the watersheds in the
region?
Degree of degradation
Which watersheds should be restored?
Degree of restorability
Continued…

OR combine questions:
Based on the condition of the watersheds and the
restorability of the watersheds, which should we
restore?
 Motivation to restore

Step 6a: Indexing the data
Convert all criteria metrics to the decision
unit.
Indexing = Relative comparison among the range of
metric values on a decision scale; = unit converter
(converts units of each criterion metric to the decision
unit).
Expert Input here
What is the decision significance of the indicator
values?
Same indicator can have different signficance for
another decision question.
Step 6b: Approach to Indexing the
Data
 Use a decision scale of 1 to 8.
 Assumption: Each criterion is of equal value or
importance.
 BUT Metrics are not looked at independently.
 Task in indexing is to define what value of each
criterion elicits the same response. Set these
values to the same index.
E.g., $1 million is a lot of money and 95 ppb of ozone is
a high ozone level (on par with $1 mil) (they both elicit a
“that’s a lot” response), so set them both to the same
index.
Step 6c: Thinking about Indexing
Range of metric/indicator values? Type of
distribution?
Double check: Compare values for criteria
pairs – same significance?
Initialize; Change later if needed.
Step 7: Preferencing
All criteria are not equally important to the
decision makers/stakeholders.
Preferencing = Relative comparison of the
importance of one criterion to other criteria.
Step 7b: Thinking about
Preferencing
Initialize by setting all criteria preferences
to equal weights (i.e., all criteria equally
important to the decision question within
each level of the hierarchy). = Equal
preference value set.
Iterate
Test different value sets
Examine indexing
Examine data – including quality assurance of
data.
Step 8: Iteration
Test different value sets
Examine indexing
Examine data
Examine data uncertainty
Re-run analysis with different “what if”
scenarios.
Sulfur Deposition Raw Data
Phosphorus Loading Raw Data
S_Dep Indexed
P_Load Indexed
P_Load and S_Dep Combined
80% P_Load, 20% S_Dep
(equally important)

S_Dep hot spot (NW PA)
determined to be more
scientifically significant
than P_Load hot spot
(Delmarva Peninsula).


Science significance
stays the same.
Decision maker
judgments alter priorities
but decision process is
transparent.
Role of experts in MIRA
 Experts in all fields of study to discuss issues:
Indicator Types; construction of appropriate
indicators?
Data* for indicators (existing, new)
Missing data issues
Scale of indicators/data
Combining public health and ecological information
Indexing data (determine relative significance of data)
Role of decision makers in MIRA
 Learn the impact of different value sets (i.e.,
relative preference weights among decision
criteria) on the decision options.
Science remains constant.
 Examine/compare the results of different value
sets.
 Make a decision after being informed about the
impacts of all the options examined.
Build decision confidence.
Provide documentation and rationale for decision.
MIRA different from other decision
support approaches…
 Hierarchy: represents decision question
 Indexing: Expert input = relative decision
significance of the indicators
 Preferencing: Decision maker/stakeholder
judgments = relative importance of the decision
criteria for this decision.
 Relative contextual analysis.
 Illustrates what/where the tradeoffs are – as
constrained by the data. – Learning.
MIRA References
 http://www.epa.gov/reg3artd/airquality/mira_descr.htm
 Cimorelli, A. and Stahl, C. (2005), BSTS 25(3): 1,
“Tackling the Dilemma of the Science-Policy Interface in
Environmental Policy Analysis.”
 Stahl, C.H. (2003), “Multi-criteria Integrated Resource
Assessment (MIRA): A New Decision Analytic Approach
to Inform Environmental Policy Analysis.” For the
Degree of Doctor of Philosophy, University of Delaware.
 Stahl, C. H. and Cimorelli, A. J. (2005), Risk Analysis
25(5): 1109, “How Much Uncertainty is Too Much and
How Do We Know? A Case Example of Ozone Monitor
Network Options.”
Part III: Logic Model Outputs
as MIRA Inputs
Program prioritization
What do we get with LMs and MIRA?
 Integration of Data and Program Activities.
Are we doing the right activities? – based on where the
“worst” conditions are.
Which activities have the greatest effect on the
outcomes/impacts we seek? – based on which
outcomes/impacts we value most highly AND the
condition data.
Which activities are dependent on which other
activities?
 Capability to prioritize program outcomes using
data.
 Transparency
 Learning
Example Logic Models
Air Quality Monitoring Logic Model
Ozone Program Logic Model
Trace monitoring activity (certification of
ozone air quality data) through Monitoring
Logic Model outputs/outcome/impacts
See Red text in following figure.
Follow black boxes within Monitoring logic
model in following figure.
Baseline
Stressors
Activities
Outputs
O3 SIP Program:
O3 SIP Program
Stressors
O3 Nonattainment
Area Designations
O3 Nonattainment
Area Designations
O3 SIP
Program
Baseline 1=
03 DV
weighted by
sensitive
population
(children,
elderly, etc.)
O3
Monitoring
Program:
Outcomes
Impacts
O3 SIP Program
O3 SIP Program
(Human Health)
Impact 1 =
Outcome 2=
O3 SIP Program
Stressor 4 = #
03
DV weighted
Human health
by sensitive
impacts from
population
O3 pollution
(children,
elderly, etc.)
Based on O3
upwind areas monitor design
designated
values, concur
attainment for with HQ on O3
O3.
design monitor
for each area.
List of DVs for each
NA area
O3 Monitoring
Program Stressors
Data Review for
Official O3
official O3 monitor monitoring data:
DV Output:
DV
O3 Monitoring
program Stressor 1
O3 Monitoring
Baseline 1 = need a = regulatory
requirement to
metric for the
certify O3 data and
accuracy of the
monitoring network calculate O3 DV.
Data Review for
official O3 DV for
O3 monitor:
Review states’
certification of
O3 data (AQS
data prior to
official use).
MIRA
Indicator
Complete and
certified O3 AQ
data (no missing
years, etc).
Data usable
for AQ
planning. A)
selection of O3
design monitor
for R3 areas.
Monitored O3
levels accurately
represent true
O3 levels for AQ
planning areas. –
Correlation
coefficient between
monitored and
other estimation
methods of O3?
Cont’d Ozone Logic Model
Show dependency of Ozone Program
activity on Monitoring certification of data.
See red underlined text in previous figure.
Trace Ozone program activity through to
its outputs/outcomes/impacts.
Follow red boxes in previous figure.
How does this connect to MIRA?
MIRA Indicator
Health indicator preferred but currently no
data/science.
Use Ozone concentration weighted by
population as surrogate for now.
Summary
 Logic models improve program understanding.
Logic models provide connection between program
activities and outcomes/impacts.
If prioritization is desired, use as MIRA input.
 MIRA approach is compatible with use of many
environmental, economic and social criteria.
Use of HRS criteria possible in 2 different ways.
Supports the use of surrogate data (using data that is
readily available).
MIRA allows transparency, learning, stakeholder
inclusiveness.
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Prioritization using Logic Models and MIRA