deepwater horizon oil leak: a decision analytic …
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DEEPWATER HORIZON OIL LEAK: A DECISION ANALYTIC APPROACH TO RESOURCE ALLOCATION
A DISSERTATION SUBMITTED ON THE TWENTY NINTH DAY OF FEBRUARY 2012
TO THE DEPARTMENT OF ENVIRONMENTAL HEALTH SCIENCES IN PARTIAL FULFILLMENT OF THE REQUIREMENTS
OF THE SCHOOL OF PUBLIC HEALTH AND TROPICAL MEDICINE OF TULANE UNIVERSITY
FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY BY
·~~ BENJAMIN CHARLES SCHULTE
DOCTORAL COMMITTEE
APPROVED:
PETER FOS, P . ., DDS, M.P.H. SARAH K. MACK, Ph.D., M.S.P.H. COMMITTEE CO-CHAIR
;i~--=--.1(._~5!....--..-::.:1\:..______ ~CE,Ph.D.
/4YJI;L " ROBERT REIMERS, Ph.D.
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ACKNOWLEDGMENTS
I would first like to extend my gratitude and appreciation to my dissertation committee. My advisor and committee co-chair, Dr. Andrew Englande, Jr., for his exceptional guidance and dedication in professional and personal mentorship over the duration of the program. Dr. Peter Fos, for his advice, support and encouragement throughout the dissertation process. Dr. Sarah Mack, for her practical and insightful perspective in approaching the dissertation. Dr. Robert Reimers for his consistent availability and motivational talks. Lastly, I would like to thank Dr. Janet Rice for her availability and assistance with the statistical portion of my dissertation.
I would like to extend my greatest gratitude towards my family and friends: My mother Mary Bechtel Schulte for her countless hours of editing, support, and love during this dissertation and throughout my life. My father, Fred Schulte and step-mother Sue Chattier Schulte for their continued support and encouragement. My brother F. Martin Schulte and sister-in-law, Thea McMahen for spurring me on in moments of doubt. To all my friends with specific thanks to James Duddy, Ester Kim, Piero Spadaro, Mikiala White, Dr. Thomas Vander Jagt, Ashley McConnell Vander Jagt, Diana Hamer, Drs. J.B. and Deborah Barbeau. A very special thanks to my Masters advisor, Dr. William Toscano, Jr. for his continued support extending well beyond my time at the University of Minnesota.
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ABSTRACT
On AprillO, 2010, The British Petroleum (BP) Deepwater Horizon oil-drilling rig
experienced a blowout resulting in the death of eleven rig workers and an oil leak of 4.9
million barrels into the Gulf of Mexico that lasted eighty-six days. It was the largest oil
spill related incident to occur in United States coastal waters. The immediate impacts of
the leak were extensive to the ecosystem, wildlife, as well as the communities dependent
on the environment and oil and gas operations for their livelihood. Moratoriums were
placed on deepwater drilling and commercial fishing operations as precautionary
measures to protect human health and to re-evaluate drilling operational procedures.
Although the full impacts associated with the Deepwater Horizon incident are still
being assessed and litigated, the incident caused harm to the coastal region communities.
BP 's lack of preparedness to handle an incident like the Deepwater Horizon event
provides strength to the idea that impacted communities must be self-reliant in preparing
for future disasters and that funds resulting from federal penalties levied against BP
should be allocated towards the communities adversely impacted by their negligence.
This idea was given additional credence by the National Oil Spill Commission's
recommendation that 80% of the Federal Clean Water Act fines be allocated towards
environmental, economic, and societal resilience measures in impacted regions in their
fmal report. The report further identifies a need to prioritize tasks and make the
recommendations binding.
This research identifies and prioritizes resilience measures in St. Bernard Parish, LA
with consideration to future oil related measures. This study developed a decision
analytic tool that creates a framework for prioritization of monetary resource allocation
that promotes long-term resilience of St. Bernard Parish with consideration to future oil
related events. The model incorporates impacts associated with the oil leak in the
recovery stage as well addresses sustained resilience deficiencies in the Parish. The study
defined long-term as the time at which initial distribution of civil penalties associated
with the Deepwater Horizon event are allocated to flfty years in the future. The Delphi
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method identified thirty-three model variables through expert input and consensus. The
created multi-criteria decision model is based on the Simple Multi-Attribute Rating
Technique (SMART) methodology. SMART determined expert value trade-offs of model
variables through a scoring system based on a ratio assessment procedure and they were
listed from highest trade-off scores to the lowest.
The results indicated a high prioritization for monetary resource allocation of
variables associated with environmental and economic stability. Variables associated
with increasing the capacity to respond to future oil related disasters ranked particularly
low in the prioritization of monetary resource allocation. The identification and
prioritization of model variables indicate a strong sentiment among expert participants to
implement more generalized resilience measures that will prepare the Parish for future
man-made and natural disasters, opposed to specifically bolstering resilience measures
pertaining to future oil related events in the Gulf of Mexico.
The thirty expert participants were placed in four expert groups: Science and
Technical representatives, Business representatives, Government representatives, and
Community Based organization representatives. To assess the experts value trade-offs of
the model variables, Kendall's Coefficient of Concordance was used to identify the level
of agreement among experts as a whole and within their respective expert group.
Agreement in ranking model variables was found for twenty-nine of thirty-three variables
assessed. The levels of agreement were higher in the individual groups when compared
to the expert group as whole. The low to moderate levels of agreement within the
individual groups indicate diverse perspectives and value trade-offs by experts in
prioritizing the identified resilience measures. The Kruskal-Wallis H-test was used to
assess agreement in median ranking of model variables between expert groups.
Agreement in median rankings was identified in twenty-five of thirty-three model
variables among the four expert groups. Disagreement was found in ranking of five
environmental variables.
This research recommends the use of civil penalties associated with the BP
Deepwater Horizon incident to implement the resilience measures identified and
prioritized in this research. A stipulation prior to implementation, however, is to conduct
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a thorough cost assessment of each resilience measure and account for projects in St.
Bernard Parish with secured funding that are found in the prioritized list within this
research. The results of both assessments will provide guidance to policy makers to
allocate available funds.
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TABLE OF CONTENTS
ABSTRACT ........................................................................................................................ 3
TABLE OF CONTENTS .................................................................................................. 6
CHAPTER 1: Introduction .............................................................................................. 8 1.1 Significance and Background of Study ..................................................................... 8 1.2 Statement ofthe Problem ........................................................................................... 9 1.3 Purpose ofthe Study .................................................................................................. 9 1.4 Research Questions .................................................................................................. 11 1.5 Assumptions of the Study ........................................................................................ 11 1.6 Definition ofTerms ................................................................................................. 12
CHAPTER 2: Literature Review .................................................................................. 14 2.1 Multicriteria Decision Analysis ............................................................................... 14
2.1.1 Simple Multi-Attribute Rating Technique (SMART) ...................................... 18 2.1.2 Delphi Method .................................................................................................. 18
2.2 Offshore Oil Related Incidents ................................................................................ 19 2.2.1 Natural Disasters and Anthropogenic Disasters ............................................... 20 2.2.2 Exxon Valdez Oil Spill ..................................................................................... 21 2.2.3 Ixtoc I Oil Spill ................................................................................................. 22 2.2.4 British Petroleum Deep Water Horizon Oil Leak ............................................. 25 2.2.5 Relevant Policy ................................................................................................. 28 2.2.6 Social Ecological Systems ................................................................................ 30 2.2.7 Degradation ofthe Mississippi Delta ................................................................ 30
2.3 Gulf of Mexico Livelihood ..................................................................................... 33 2.3.2 Regional Economic Stability ............................................................................ 33 2.3.3 Environment ...................................................................................................... 37 2.3.4 Societal Issues ................................................................................................... 40
2.4 Disaster Management .............................................................................................. 42 2.4 Disaster Management Model ............................................................................. .42
2.5 Application of Multiattribute Decision Modeling to Oil Related Incidents ........... .43 2.5.1 Analytic Network Process and Resource Allocation ...................................... .44 2.5.2 SMART and Resource Allocation .................................................................. .45
CHAPTER 3: Research Methodology .......................................................................... 47 3.1 Model Development ................................................................................................ 47 3.2 Structuring Decision Model ..................................................................................... 51 3.3 Survey Development. ............................................................................................... 52
3.3 .1 Internal Review Board Approval ...................................................................... 52 3.3.2 Selection of Experts .......................................................................................... 52
3.4 Data Collection ........................................................................................................ 55 3.4.1 Weighting ofModel Variables ......................................................................... 56
3.5 Data Analysis ........................................................................................................... 58 3.5.1 Kendall's Coefficient of Concordance (W) ...................................................... 58 3.5.2 Kruskal-Wallis H-test ....................................................................................... 59
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TABLE OF CONTENTS (Continued)
CHAPTER 4: Research Results .................................................................................... 60 4.1 Response Rate .......................................................................................................... 60 4.2 Delphi Method Results ............................................................................................ 61
4.2.1 Round One Results .....................................................•..................................... 61 4.2.2 Round Two Results ........................................................................................... 65
4.3 Simple Multiattribute Technique Results ............................................................... 71 4.4 Hypothesis Testing ................................................................................................. 73
4.4.1 Kendall's Coefficient of Concordance (W) Results ........................................ 73 4.4.2 Kruskal-Wallis H-test Results ......................................................................... 79 4.4.3 Hypothesis Test Conclusion and Summary ..................................................... 82
4.5 Prioritized List of Resource Allocation and Cumulative Percent List .................... 84 4.5.1 All Expert Response ........................................................................................ 85 4.5.2 Group Responses ............................................................................................. 88
4.6 Conclusion of Research Results ............................................................................. 98
CHAPTER 5: Discussion .............................................................................................. 102 5.1 Overview ofResults ............................................................................................... 102 5.2 Hypothesis Testing ................................................................................................ 106 5.3 Review of Objective Ranking ................................................................................ 107 5.4 Review of Environment Objective and Sub-objectives and Results ..................... 109 5.5 Review of Logistical Capacity Objective and Sub-Objectives and Results .......... 110 5.6 Review of Economic Objective and Sub-Objectives and Results ......................... 110 5.7 Review of Societal Impacts Objective and Sub-Objectives and Results ............... 111 5.8 Policy Implications ................................................................................................ 112 5.9 Public Health Pertinence ..............................•......................................................... 116 5.10 Conclusion and Summary .................................................................................... 118
CHAPTER 6: Limitations and Recommendations .................................................... 123 6.1 Limitations ............................................................................................................ 123 6.2 Recommendations ................................................................................................. 124
APPENDIX A: Internal Review Board Approval Letter .......................................... 125
APPENDIX B: Delphi Questionnaire Round One ..................................................... 126
APPENDIX C: Delphi Questionnaire Round Two .................................................... 139
APPENDIX D: SMART Questionnaire ...................................................................... 150
APPENDIX E: Raw Data ............................................................................................. 157
REFERENCES ............................................................................................................... 169
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CHAPTER I
INTRODUCTION
The global demand and use of oil as an energy source has led to extensive searches
for oil reserves. Offshore exploration and extraction of oil is common worldwide and the
United States Department ofEnergy and Department oflnterior anticipate significant
growth ofthis practice in the future (Department ofEnergy, 2011). The United States
Outer Continental Shelf operations in the Gulf of Mexico yield 379,820 thousand barrels
per year and account for thirty percent of oil production in the United States (BOEMRE,
2011). Though offshore drilling is an economically viable process to extract oil, there are
associated ecological and subsequent societal risks should an incident occur.
On April10, 2010, the semi-submersible Deepwater Horizon oil drilling rig operated
by British Petroleum (BP) experienced a blowout which subsequently cost the lives of
eleven employees and sank the drilling rig. The leak continued for eighty-six days
releasing 4.9 million barrels of oil into the Gulf of Mexico. The short-term impacts of the
incident were significant in Gulf coast communities. Concerns associated with the safety
of oil drilling rig operations as well as the lack of preparedness and capacity by British
Petroleum to effectively respond the Deepwater Horizon incident resulted in a Federal
mandated moratorium on deepwater oil drilling in the Gulf of Mexico. Concerns related
to the contamination and bioaccumulation of oil in aquatic gulf species commonly
consumed by humans as well as the potential implications that large quantities of oil
would have on the complex and fragile Gulf ecosystem resulted in a moratorium on
fisheries and deepwater oil drilling operations. Due to the importance of oil and gas and
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fisheries in coastal communities livelihood, the economic impact of the moratoriums
were significant to the well being of coastal populations (Austin, et. al., 2008)
As any disaster or major event, the Deepwater Horizon incident was unexpected. As
evidence of the duration and quantity of oil leaked, the responsible party's (British
Petroleum) preparedness plan to handle such an incident was insufficient. BP's
negligence highlights the need for coastal Parishes in Louisiana to be prepared and self
reliant to respond to any future event of this nature. Ensuring adequate preparation and
capacity to respond to such an event will greatly mitigate the impacts on a fragile
ecosystem and the economic stability of coastal communities.
Resilience is a principle concept in this research, thus it is important to defme the
concept. Though organizations define resilience differently, the fundamental meanings
are the same. The Community and Regional Resilience Institute defines resilience as
"The capability to anticipate risk, limit impact, and bounce back rapidly through survival,
adaptability, evolution, and growth in the face of turbulent change".
In addition to the importance of Parish preparedness to mitigate the short term
impacts of future disaster or incident, the Deepwater Horizon incident further highlighted
the lack of resilience in coastal Louisiana and its susceptibility to disasters. The
extensive canal and pipeline systems utilized by the oil and gas industry have resulted in
a high rate of coastal erosion and environmental degradation of wetlands. This is critical
as the coastal ecosystem and wetlands provide an important natural buffer against
hurricanes.
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The historical connection between oil and gas operations and environmental
degradation in coastal Louisiana as well as British Petroleum's negligent operations and
preparedness plan to handle a drilling incident, provides an argument that a portion of the
penalties associated with the Deepwater Horizon incident be allocated toward the
promotion of resilience in areas impacted by a large scale oil leak. This concept of
resource allocation has been backed by the National Commission on British Petroleum
Deepwater Horizon Oil Spill and Offshore Drilling's fmal report.
This research created a decision model, using Simple Multi-Attribute Rating
Technique (SMART) that can be applied to prioritizing resource allocation of federal
fines from the BP deepwater Horizon incident in a fashion that promotes resilience
measures towards future 'oil related incidents in Louisiana coastal communities. This
research focuses specifically on the needs of St. Bernard Parish, LA (St. Bernard). The
decision analytic tool elicits the preferences and value trade-offs of experts with
significant knowledge of the issues facing St. Bernard to create a decision analytic tool.
The multi-criteria decision model establishes a priority based ordinal ranking and
preference structure of key aspects necessary for long-term resilience in St. Bernard
Parish following the BP Deepwater Horizon incident. These aspects will be delineated as
objectives and sub-objectives of an optimal response. The objectives to be included in
this study include the environment, economic stability, logistical capacity for disaster
response, and societal impacts.
The ordinal ranking will be accomplished by collecting insights from experts
obtained through an interview and questionnaire process. Experts will be selected from
the fields of public health, environmental science, academia, oil and gas, elected
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government officials, government employees, engineers, community oriented non-profits,
and representatives from the fishing industry. At the conclusion of the interviews, a
multi-criteria value tree will be constructed. The priority of objectives of a response will
be established through a weighting process, based on the expert input.
The following research questions will be addressed:
1. What are the model variables to be addressed in this multi-criteria value model that promotes long-term resilience with consideration to future oil related events in St. Bernard Parish, LA?
2. What are the differences, values, and preferences across and among the expert's identified expert groups?
3. Can a decision model provide a useful tool and template to decision makers for promoting resilience with consideration to future oil related events?
4. What are the weights of model variables and how are they prioritized?
5. What can be learned through the development of this model?
The assumptions of this study are:
• The data collection process requires professional knowledge that is consistent in the experts' judgment.
• The experts are the best-qualified individuals locally available to respond to the questionnaires.
• The experts tend to make consistent judgments with one another.
• The diversity and scope of the educational and professional background of the experts reflect their expertise as attributed to their roles as members of their profession.
• Monetary resources resulting from penalties associated with the BP Deepwater Horizon are limited and need to be maximized to best promote resilience.
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Dermitions:
The definitions and use of terms that are frequently referred to throughout this research are below:
Multi Attribute Decision Analysis: "an umbrella term to describe a collection of formal approaches which seek to take explicit account of multiple criteria in helping individuals or groups explore decisions that matter"(Belton and Stewart 2002).
Analytical Hierarchy Process (AHP): A form of Multi-Attribute Decision Analysis that measures alternatives in a pairwise comparison. The comparisons indicate the expert's judgment and values on prioritizing alternatives (Saaty, 1980). In terms of this research the Analytical Hierarchy Process will be used to create a prioritized list to decision makers for allocating limited resources in the event of a man-made water related disaster. The specific type of Analytical Hierarchy Process used in this research is the Simple Multi Attribute Rating Technique (SMART).
Simple Multi-Attribute Rating Technique (SMART): A technique of multi attribute decision model that assesses the utility of alternatives in a direct rating technique (von Winterfeldt, 1986).
Disaster: "A disaster is a sudden, calamitous event that seriously disrupts the functioning of a community or society and causes human, material, and economic or environmental losses that exceed the community's or society's ability to cope using its own resources" (IRCRC, 201 0). The International Red Cross and Red Crescent provide an equation to identify the relationship of the components of a disaster: Vulnerability+ Hazard/Capacity =Disaster
Disaster management: "The managerial function charged with creating the framework within which communities reduce vulnerability to hazards and cope with disasters" (FEMA framework, 2011).
Deep-water oil drilling: Oil and gas exploration at water depths greater than 156 meters or 500 feet, as defmed by the US government temporary moratorium placed on deepwater oil drilling operations in May 2010 (Department ofthe Interior, 2010).
Oil Leak: The "sudden, localised release of petroleum into the environment. In the case of major spillages, the quantities greatly exceed what the local environment is able to assimilate without resulting in damages" (CEDRE, 2011 ).
Values: The opinions of experts on what they see as the most vital aspects of a decision.
Objective: The individual components that when pieced together, comprise the decision in question (Ramanathan, 2001).
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Sub-objective: The variables that comprise of the objectives in the decision model.
Trade-offs: A measurement of potential gain from selecting one objective at the detriment of a different objective (Keeney, 2001 ). Trade-offs in decision modeling identify expert's values.
Weight: The numerical value experts give to their perceived importance of an attribute. Weighting in Multi Criteria Decision Analysis is the tool used to assess Trade-offs (Phillips & Stock, 2003).
Resilience: The Community and Regional Resilience Institute defmes as "The capability to anticipate risk, limit impact, and bounce back rapidly through survival, adaptability, evolution, and growth in the face of turbulent change".
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CHAPTER II
LITERATURE REVIEW
2.1 Multi Criteria Decision Analysis:
The nature of decision-making is often a complex process involving interconnected
issues of which outcomes can have far reaching impacts on the experts involved.
Decision makers must approach a decision in the most logical and infor:ined fashion to
fully understand the depth of the decision and subsequent ramifications. Multi Criteria
Decision Analysis (MCDA) is a methodology that dissects a complex decision into its
most basic elements of importance. MCDA presents a tool that considers quantitative and
qualitative information in the decision.
In the process of decision model development the attributes and alternatives of the
decision are separated into objectives and sub-objectives and placed into a graphic model
known as a value tree. A decision tree is a graphical tool that aims to dissect components
of a decision in a logical fashion for decision makers to have as clear of an understanding
of outcomes from a decision as possible.
Once the attributes have been placed into the value tree, experts are interviewed and
asked to rank and rate the sub objectives in what they see as the most important to the
least important. This results in the development of an order of priority with the
objectives and sub-objectives. At the conclusion of the interviews, the researcher will
perform statistical analysis, and weights will be assigned to the objectives and sub
objectives. The sub objective and objective with the largest weight represents what the
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experts collectively see as the most important areas to consider in the resource allocation
decision (Y oe, 2002).
A benefit of decision modeling is the determination of objective utility scores that are
used as a method to quantifY experts' values for decision makers. Objective utility scores
scientifically dissect a problem and express the values of experts in the field. This is a
helpful tool for the decision maker to have in explanation of the rational and logic behind
the decision. The decision model considers values placed on alternatives, or value trade
off's, by the identified experts. Value trade-offs can best be described as the priorities of
the experts in the creation of the model and the decision maker in the choice of an
alternative. It is the importance placed in selecting one alternative at the detriment of the
other alternatives. The decision analysis assesses the decision maker's value trade offs of
the alternatives in the decision opportunity through an objective score known as a utility
or value score (Keeney, 1982). Both utility and value scores measure the decision
maker's preferences for decision alternatives. They differentiate in that utility scores
measure the preference of an uncertain alternative by a decision maker, while value
scores measure preference in certain alternatives. The use of a value model requires the
decision maker assess their value trade-offs and willingness to accept risk (Keeney,
1982).
The utility score and the subsequent value trade-off analysis of the experts will be
assessed through a series of questionnaires, conversation with the experts, and statistical
analysis of their response to the questionnaire. A portion of the statistical analysis will
include weighting the expert responses. The weighted values will provide important
insight to the decision maker on the value that the experts from a given field rank as a
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priority. This process allows the decision maker to gain knowledge of the basic
components of the decision, to provide the decision maker an opportunity to understand
the values of the experts, and to provide the decision maker time to consider the decision
and provide insight into the decision makers own values as applied to the event (Alemi,
2007).
The purpose of this model is to provide the decision maker(s) a prioritized list
detailing the values that are likely to be affected in the event of an anthropogenic disaster
effecting water quality (similar to that of the BP oil leak). The allocation of resources
during a disaster of this nature will ultimately be dictated by elected officials who may
not have extensive knowledge of oil related disasters. Governing officials in the United
States are elected on a broad platform and may have limited understanding of a given
problem.
Further, it is not unusual for human nature to quickly respond to a given situation
without thoroughly thinking the process through. This process indicates reactive, not
proactive decision-making, also referred to as alternative-focused thinking (Keeney,
1996). Decision-making requires the decision maker to fully understand the components
of a problem and what experts have to gain or lose by the outcome of the decision. If the
components are not fully understood by the decision maker, the decision maker values
may reflect only his or her values, and the full breadth and complexity of values may not
be displayed in the outcome of the decision (Alemi, 2007). Should this occur, it does not
mean that the decision is right or wrong but rather ill informed, carrying a potential for
undesirable outcomes. In context of this research, the complexity of the social ecological
system needs a multidisciplinary approach to address the allocation of resources
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following an oil leak. A MCDA framework can address this need. An additional benefit
to implementing a structured decision model is that it allows the decision maker an
objective and valuable tool to reference should the decision come into question.
Two critical factors are necessary in order to confirm the value of the decision model.
The first is the process of selecting experts to participate in the ranking of the values.
The second is the need of the researcher to maintain objectiveness when interviewing the
experts (Alemi, 2007). Those chosen to participate in the interview session will be
divided into scientific/technical, industry, citizen, appointed officials, and regulatory
expert groups. The pool of experts was chosen based on their education, years of
experience, knowledge of problems facing St. Bernard Parish, and involvement in their
given field throughout the ongoing response to the BP oil disaster in the Gulf of Mexico.
They will initially be identified through the researcher's attendance at public meetings,
conferences, other outreach programs, and recommendations. In addition to involvement
in the current response efforts, professional experience and education level are
parameters that will be considered in defining and identifying experts. Those selected
will comprise a pool stemming from a diverse background in engineering, environmental
science, disaster management, oceanography, local and state government representatives,
natural resource industry managers, healthcare industry managers, fisherman, citizens,
and representatives from the tourism industry. During the interview stage, the researcher
asked the interviewee of knowledge of other persons they consider experts in their field.
Those suggested were evaluated on whether or not they qualify as experts in this
research. Those who qualified as experts were then contacted in order to gauge interest
in participation in this research.
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The use ofMCDA has been well established and is an accepted theory in the decision
sciences. It has been used in the fields of health care, engineering, fmancial analysis,
military, and the environmental fields (von Winterfeld & Edwards, 1986). There are
many valid methods to conduct a MCDA. This research will use the Simple Multi
Attribute Rating Technique.
2.1.1 Simple Multi-Attribute Rating Technique:
Simple Multi-Attribute Rating Technique (SMART) is designed for decisions that
require a prioritization of objectives. SMART is a type of Analytical Hierarchy Process
(AHP). AHP develops a linear additive model based on pair-wise comparisons (Dodgson
et al., 2001 ). The scope of this research is to provide a prioritized list of measures in St.
Bernard Parish to promote future resilience against future oil related incidents.
2.1.2 Delphi Method:
Due to the recent event and the nature of this project it was important to establish
consensus among experts on the important aspects of resource allocation following an oil
leak. In this specific research, the sub-objectives will be assessed by the experts. The
Delphi Method is a process that establishes consensus among experts in a decision model
(Chocholik et al., 1999). Three characteristics that are useful in establishing consensus
are a sequential questionnaire, iteration, and controlled feedback (Jones & Hunter, 1995).
Similar to the SMART technique, the Delphi method will be completed quasi
anonymously through a questionnaire process (Hsu, 2007). This method will allow the
selected experts to express their thoughts of what components are important to the
research without influence from their peers. The Delphi method is often completed in
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two or three rounds of questionnaires. The expert will be asked to use a Likert scale to
judge how important they think a given sub-objective is to resource allocation following
an oil leak. In the scale, the rank of 5 will reflect strong agreement while the rank of 1
will reflect strong disagreement. The questionnaire allowed for the expert to provide
additional variables (sub-objectives) that were not listed, but seen as important to the
expert. The Delphi method is an iterative process, following the completion of each
round, the experts were given the weighted results of the prior round and allowed to
assess the group results and change their response should they feel it necessary (Hsu,
2007).
The questionnaire rounds continued until consensus is met or participants withdraw
from the process (Hasson et al., 2001). Consensus in the Delphi method is measured
through the use of standard deviation and a ranking of 3.5 or greater on a 5 point Likert
scale (Turner, 2002). Linstone and Turoff (2002) state that the sample size for the Delphi
model to be relevant is between ten and fifty participants. This research will have fifteen
participants for the Delphi portion of the study. Expert selection for the Delphi method
was based on knowledge and expertise in the fields that pertain to this research (Hasson
et al., 2001 ).
2.2 Offshore Oil Related Incidents in the United States:
Disasters pertaining to public health, and the subset of environmental health, can be
categorized into two distinct types: natural disasters and man-made (anthropogenic)
disasters.
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Natural disasters consist of incidents like flooding, hurricanes, droughts, earthquakes,
tornadoes, fires, and landslides. These disasters occur as a result of nature and the
placement of communities in vulnerable locations (American Red Cross, 2003).
Examples of major and recent natural disasters include the 2011 earthquake and tsunami
impacting Japan, 2010 earthquake in Haiti, 2005 Hurricane Katrina in New Orleans, LA,
and the 2004 earthquake and subsequent tsunami in Southeast Asia. All four events had
catastrophic tolls on the environment, economic stability, and populations. In addition,
the disasters exposed weakness in the logistical capacity to respond to a disaster.
Anthropogenic disasters can result from industrial accidents, engineering failures of
infrastructure, and terrorism. Similar to natural disasters, a potential exists for
environmental, economic, and infrastructural impacts. They can effect air and water
quality, which after exposure may have adverse effects to human health and the
environment (American Red Cross, 2003). This research is based on anthropogenic
disasters with the focus on oil disasters resulting from deepwater oil drilling incidents.
There are similarities and differences seen between oil related incidents in the Gulf of
Mexico. The purpose ofthis section is to provide a brief review ofthe Ixtoc I oil disaster
in the Gulf of Mexico in 1979, and the BP Deep Water Horizon oil disaster in the Gulf of
Mexico in 2010. The similarities and differences between the two oil disasters will be
addressed. Further the comparison will provide insight to the potential long-term
repercussions that may result from the BP Deep Water Horizon oil disaster. In addition
to the review ofthe two aforementioned oil disasters and the Exxon Valdez oil spill in
1989 will be discussed as to the insights as well as the policy implications resulting from
that event.
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2.2.2 Exxon Valdez Oil Spill:
On March 24, 1989, the Exxon Valdez, an oil tanker with the capacity to carry 1.48
million barrels of oil, struck the Bligh reef island in Prince William Sound, Alaska. This
resulted in the release of more than 260,000 barrels of oil into the sound. (NOAA, 2010).
There were large environmental, economic, and social implications associated with the
spill. Though difficult to quantify due to natural variability and a lack of baseline data,
the environmental impacts were estimated to be far reaching to the ecosystem (EVOSTC,
2009). The estimates report that 250,000 seabirds, 1,000 sea otters, 151 bald eagles,
1,1 00 marbled murrelets were killed as a result of the oil the months following the spill.
The assessment of the long term impact of the Exxon Valdez spill is ongoing. As of 2004
it was estimated that 0.2% of oil from the 1989 spill was present on an estimated 7.8
hectares of shoreline (Short, et. al). The twenty year report produced by the Exxon
Valdez Oil Spill Trustee Council (2009) reported that the degradation ofthe present oil is
taking place at a rate of 0-4% per year. It is anticipated to take decades for the remaining
oil to degrade completely (EVOSTC, 2009).
The Exxon Valdez spill had societal impacts on the communities that use Prince
Williams Sound for their livelihood and sustainability. Fifteen Alaskan Native
communities rely on the area affected by the spill for subsistence fishing (EVOTSC,
2011 ). Following the spill, the fishing harvests were reduced in numbers of fish counts
and new concerns developed which were associated with human consumption and
contaminated fish species (EVOTSC, 2011). There was a prevalence of Post Traumatic
Stress Disorder (PTSD) in 25% of native Alaskan communities, which were dependent
on fishing for their livelihood (Palinkas, et.al., 2004). As of2009, the human services
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(commercial and subsistence fisheries as well as tourism) are still classified as being in
the recovering stages (EVOTCS, 2009). The human services will not be considered
recovered until the resources (fish counts) on which they are dependent fully recover. A
fish species of particular concern is the Pacific Herring. It is commercially fished for
food and bait as well as considered important in the food chain for Prince Williams
Sound. This species has had a great deal of difficulty in recovering (EVOTCS, 2009).
Through a series of civil settlements, Exxon was required to pay $900 million for
restoration of injured natural resources, $25 million in criminal plea agreement, and $100
million in criminal restitution (EVOSTC Status report, 2009).
The policy implications resulting from the Exxon Valdez are significant in regards to
oil disaster management. Prior to the 1989 tanker spill, there was no comprehensive
legislation in the United States that addressed the handling of an oil spill response. The
Oil Pollution Act (OP A) of 1990 was the first such legislation that established a more
comprehensive framework which addressed oil spill response, prevention, and liability
(Lee & Bridgen, 2002). OP A places the burden of restoration and rehabilitation of an
effected environment on the guilty party associated with an oil spill or leak. OP A places
a $7 5 million limit on responsibility to private parties.
2.2.3 Ixtoc I Oil Disaster:
To bring perspective to this research a short synopsis of the Ixtoc I oil disaster in the
Gulf of Mexico will be given. The literature review found limited data regarding the
damage associated with the Ixtoc I disaster on the Mexican coast. Studies completed by
the United Nations Environmental Programme estimated approximately 29,000 metric
22
tons of oil washed ashore to the Mexican coast with wide spread acute toxicity to shrimp
larvae and the ghost crab (Ocypode quadrata) populations (Jemelov and Linden, 1981).
A comprehensive study assessing the economic and environmental impacts of the Ixtoc I
oil disaster was conducted by the United States Bureau of Land Management (BLM) on
the Texas coast, approximately 1,000 kilometers from the Ixtoc I disaster. The BLM
study is the primary source of information as to the impacts of the disaster.
In summary, in 1979 an exploratory well, Ixtoc I was being drilled by a state run
Mexican company, Pemex, approximately 100 kilometers off ofthe southern coast of
Mexico. A loss in drilling mud circulation resulted in an unequal distribution of pressure
within the operating system, causing the well to blow. The blowout preventers were
unable to stop the flow of drilling mud, oil, and gas rising to the surface of the drilling
platform. This led to a fire on the platform and the subsequent sinking of the platform.
The piping system used to transport the oil to the surface was damaged as the platform
was sinking, which resulted in oil leaking into the Gulf of Mexico. The limitations in
technology available at that time prevented the effective containment of the released oil.
The event resulted in 3 million barrels of oil released into the Gulf of Mexico over the
294-day duration of the leak (June 3, 1979-March 23, 1980) (Jemelov & Linden, 1981).
The BLM study used macro flora and organisms found living in sediment as
biological indicators in assessment of the impact of the Ixtoc I oil disaster. Data for the
macro flora was available for two years prior to the oil disaster. Oil was found in sheens
and on portions of the coastline of Texas. The study indicated a drop of nearly 33% of
taxa between the pre and post oil leak monitoring. However, the study could not
definitively state that the decrease taxa was a result of oil, as no Ixtoc I oil was found in
23
the sediment (BLM impact assessment, 1982 & Marine Pollution Bulletin, 1982). The
alternative hypothesis given to the reduction of species was the seasonal fluctuation of
dissolved oxygen levels, seasonal variations in species populations, and the impact of a
tropical storm several months prior (BLM impact assessment, 1982 & Marine Pollution
Bulletin, 1982).
The economic impact to Texas resulting from the leak and as assessed by the BLM,
was significant. This was largely in part due to the public perception of the potential
health effects associated with the oil, although no dollar value impact was estimated
(BLM impact assessment, 1982). The literature review did not indicate sufficient human
health research in relation to the Ixtoc I oil leak. However, the negative impact on the
recreation industry in the coastal regions of Texas was estimated at $3.1 million (BLM
impact assessment, 1982). The impact on the tourism industry was separated into three
categories: automotive, food, and lodging. The automotive losses were estimated
between $863,000- $954,000; the restaurant industry losses were estimated at
$1,093,000-$1,208,000; and lodging losses were estimated between $1,709,000-
$1,889,000. The BLM report did not discuss the loss of jobs associated with the service
industry. There were no significant direct or indirect economic impacts on the fisheries
sector (BLM Impact assessment, 1982).
The Ixtoc I oil leak is particularly pertinent as an example to this research as it
provides insight to aspects of potential impacts of the BP oil disaster as 1) the leak
occurred in the Gulf of Mexico providing an example of similar environmental conditions
and 2) prior to the BP disaster, the quantity of oil leaked was the largest recorded in the
Gulf of Mexico. There are notable distinctions between the two disasters. The Ixtoc I
24
operation was drilled at depths of approximately 165 feet, which is considered a shallow
water oil well. The Deep Water Horizon operation was drilled at approximately 5,000
feet below sea level, which is considered a deep water oil well. A second distinction
between the Ixtoc I and the Deep Water Horizon disasters was the introduction of oil
from an accident involving the Burmah Agate oil tanker and a freight boat on the outer
continental shelf. This accident occurred concurrently with the Ixtoc I disaster and killed
31 crew members aboard and resulted in the release of 2.6 million gallons of oil into the
Gulf of Mexico. The BLM study considers the influence of this accident in the
environmental and economic portion of the impact assessment.
Following the Ixtoc I oil leak, lawsuits were placed on the responsible party by the
public and private groups based in the United States and based on the Foreign Sovereign
Immunities Act (FSIA) of 1976. Due to the unclear authority ofFSIA in the Ixtoc oil leak
case, Mexico and the United States signed an Agreement on Cooperation Regarding
Pollution of the Marine Environment by Discharge of Hydrocarbons and Other
Hazardous Substances. This agreement provides a framework to jointly address pollution
risks (West, 1981).
2.2.4 BP Deep Water Horizon Disaster:
The Gulf of Mexico is host to 3,858 active oil platforms, which provides for 25% of
the output of oil production in the United States. The oil production forecast for the Gulf
of Mexico is projected to be 1.8 million barrels of oil per day by 2018 (Material Mineral
Services, 2008). There are ~o primary well drilling methods, shallow-water drilling and
deep-water drilling. Due to engineering complications involving depth and pressure,
25
deep water drilling has been considered to have a higher risk of accidents (Barker, et.al,
1989 & MMS, 2009).
In April of2010 a major oil drilling disaster occurred in the Gulf of Mexico. British
Petroleum's Deep Water Horizon drilling operation, located in the Macondo prospect,
experienced an explosion that caused the death of 11 oil rig workers and injuries to 17
others. The explosion and subsequent sinking of the oil platform caused the most
extensive oil spill in the history of oil exploration in the Gulf of Mexico. Though the
exact quantity of oil released from the leak will never be accurately accounted for,
estimates from a federal panel, the Flow Rate Technical Group, indicate approximately
4.9 million barrels of oil were released into the Gulf of Mexico (Department oflnterior,
2010).
The flow of oil into the Gulf of Mexico continued for 86 days as a result of
ineffective strategies to cap and seal the well. There were multiple attempts to stem the
flow of oil following the blowout. The first of these methods involved the use of
underwater robotics to close the blowout preventer mechanisms; this method had never
been attempted in a deep-water spill and ultimately failed due to water depth and
pressure. Following this attempt, a second method was tried using a large constructed
piece of steel referred to as the containment dome. It was lowered over the leak in an
attempt to funnel the oil into lines that would transport the oil to a storage boat on the
surface of the water. This attempt was unsuccessful as a leak formed in the piping and
the escaping gas and cold temperature formed methane hydrate crystals that blocked the
pipelines (Guardian, 2010). Following the containment dome failure, a smaller version
of the dome referred to as the 'top hat' was constructed and used. This attempt differed
26
from the containment dome primarily in size. The containment dome was approximately
forty feet tall while the top hat was just five feet tall. The top hat was designed so that
less salt water would be in the containment area, thus reducing the chance of methane
hydrates forming. However, the method failed due to the inability to create a proper seal
around the leaking pipe, which resulted in the continued flow of crude oil into the Gulf
(Washington Post, 2010). There were several additional attempts at stemming the flow
of oil that had limited success, including the 'static kill' method that forced dense drilling
mud through the blowout preventer at high pressure in order to 'smother' the oil being
released (Guardian, 2010). The Deep Water Horizon well was declared 'dead' on
September 19, 2010, when a reliefwell that began drilling on May 2, 2010, was
completed and tested for leaks (BP press release, 9/19/2010).
The account of the oil disaster in the Gulf of Mexico by British Petroleum was a
dynamic process. The gathering of information and facts known about the disaster would
vary among sources. There were inconsistencies in data made available to the public by
the federal government and British Petroleum. This, in part, can be attributed to the fluid
nature of a disaster of this sort. An example of inconsistent and questionable information
being published came from a government report released in August 2010 stating that 75%
of the oil from the leak was no longer in the Gulf of Mexico. The government asserted
that the oil had been skimmed, evaporated, or degraded in the ocean with the assistance
of aquatic microorganisms (PBS.org, 201 0). The government's account of oil from the
leak is illustrated in the graph below (SSWG, 2011):
27
Deepwater Horizon Oil Budget
R.e;idu;J! inc:lud(::; oil
t:h.H is en or.:tJ.5~ lwlovu·
l.h;::· surfi'Jcc• a~; lif;ht .she~n and ··.tt.H:-:..1thered ........._ RJ.':.ttlu:·l •
l.:n bdl:;, lla::.. ·.,vc;~h•:d a:;hcq·;::· cr bc•f:n coll:=c:ed from :he
slwre, or is burie~l in :3and and sE·dimfniS-
)6~;.
Ev<~pQ!1ited oj.·· ·
"~~~-8~~-s .. ~.~-~ . '~"".;"~~ Oi~pt:r~r:-d-.
16%
··-.. Unofi~c'
Cot'r'lmand ~-.--.....-."" R'~ !· pon~.e
Op~r~tio"'
' ..' ···h.-·n"l.i.:-1'1·. ··•
*oil in :he~.1::! 3 c.at.;:gori;:s i.r, cunenth' bt?ing de-graded nati.Jrally.
ShO'i'lS current best estimates of \•vhat f·F~ppened to) the oil.
This report was widely questioned and criticized by the scientific community and
those involved in the clean up (CBS news, 2010, Miami Herald, 2010).
2.2.5 Relevant Policy:
There are two pieces of legislation that play a major role in the collection of funds for
allocation to regions to impacted regions of the BP Deepwater Horizon oil disaster: The
Oil Pollution Act (OPA) of 1990 and the Clean Water Act (CWA). Following the Exxon
Valdez oil spill of 1989 Congress enacted a comprehensive approach to oil spill response.
In financial terms, OP A taxes the responsible party 18.7 5% per barrel based off of an
average cost of barrel on the market. In this case, BP would be responsible for the oil
spill of 4.9 million barrels at the average cost of $70 per barrel, which would be taxed at
18.75%. The organization's liability based on the OPA methodology is approximately
$64.3 million.
Following the oil leak the methodology used to determine the CWA fines was the
premise on which a $20 billion escrow account was established. The calculation for this
28
escrow fund was based on the quantity of barrels of oil leaked. The CWA has established
a fine of $4,300 per barrel of oil resulting from an oil disaster. In this instance the escrow
fund was established at the point where an estimated 4,765,819 barrels leaked equating to
the fine of $20 billion dollars (Donovan Law Group, 2010). This established fund is
referred to as the British Petroleum Oil Spill Victims Compensation Fund and controlled
by an independent mediator, Kenneth Fienberg.
The Natural Resource Damage Assessment (NRDA) was initially created in the
1970s to assess the damages associated with a natural resource incident. NRDA
implements knowledge from science, engineering, and economics to develop steps to
restore an injured resource to its levels prior to the incident. The NRDA process is
promulgated by NOAA. Further, NRDA requires that the responsible party compensate
the communities affected by the injury until the damage has returned to pre incident
levels (Lee, 2002). The four stages of the NRDA process are: Pre-assessment; Injury
determination and quantification; Damage assessment; and Restoration implementation.
At the time of this writing, the NRDA process for the Deepwater Horizon was not
complete.
As of March 24, 2011, BP announced claims and government payouts totaling $5.5
billion (BP Press Release 3/24/2011). The amount of$4,042,557,803 was distributed to
individuals and businesses. The amount of$1,191,693,235 was allocated to state and
local government for advances and claims. The amount of$256,898,772 was designated
towards "Other Payments". Other Payments is reference to payments made for the
NRDA process, research, tourism, and behavioral health.
29
2.2.6 Social Ecological Systems and Resilience:
Complex adaptive systems (CAS) are dynamic systems that underscore the
interconnectedness of elements that comprise a greater issue (Dooley, 1996). CAS, in
context of human and nature, have characteristics of diverse and individual components,
interactions of these components, and resulting outcomes of these interactions which lead
to the development of new sub-components. (Holling 2001, Levin, 1999). The three
properties that address adaptive capability are the availability of a system to incur change
("the wealth of the system"), the degree of connectedness of the system (controllability),
and adaptive capacity (resilience) (Holling, 2001).
Sustainability is the term the Social Ecological System (SES) uses to describe the
complex and adaptive interactions between ecological, economic, and social systems.
The SES is comprised of subsystems that have internal variables. When the internal
variables interact with each other, the outcomes ofthe greater SES are created, which
cyclically affects the subsystems (Ostrom, 2009). Resilience in Social Ecological
Systems, as defined by Walker et al. (2004) is "the capacity of a system to absorb
disturbance and reorganize while undergoing change so as to still retain essentially the
same function, structure, identity, and feedback". In this research, theSES framework
was used to assess the resource allocation in order to promote resilience in Louisiana
parishes following an oil spill.
2.2. 7 Degradation of the Mississippi Delta:
The Mississippi River drains an area of 3,344,560 km2 and is the largest river system
in North America. The Mississippi Delta accounts for approximately 22 percent of all
30
coastal wetlands in the continental United States (Gosselink, 1984). New Orleans was
settled by Jean-Baptiste, LeMoyne de Bienville in 1718. Soon after the settlement of
New Orleans, levees were established for agricultural and expansion purposes. In 1904, a
method was created by Baldwin Wood to drain the wetlands which further promoted
growth, and subsequent ecological stress in the region (Lake Pontchartrain Foundation,
2011). Extensive levee systems were constructed for flood control in the 1930's. This
altered the distribution of Mississippi river sediment, preventing the development of new
deltaic lobes, the natural land building process in delta systems. This has subsequently
resulted in the primary cause ofland loss in coastal Louisiana.
In 1901, oil was discovered in Louisiana. As the country became increasingly
dependent on oil, the methods used to extract and ship oil became an additional factor in
the degradation of the delta. The initial fmd of oil was inland and the dredging to deepen
the existing canals as well as the construction of new canals to expedite the transportation
of oil, led to the rapid wetland loss (Gosselink, 1984). As the oil industry rapidly
developed, there was an increase :in harvesting of Cyprus trees in the delta, which led to
salt water intrusion. When the inland supply of oil became depleted, the oil industry
began to explore offshore drilling and extraction processes (Gosselink, 1984). This led to
extensive engineering of canals to accommodate the increase shipping traffic from the
extraction site to land for processing.
Invasive species has, and continues to contribute to delta degradation. Nutria were
initially released in the delta to provide fur trappers in the region as a new source of pelts.
The nutria feed off of the vegetation in the wetlands and only natural predators are
alligators. With a receding demand for pelts, Nutria population growth has drastically
31
increased. Nutria populations affect an estimated 100,000 acres of coastal wetland
(USGS, 2000).
In 2005 Hurricanes Katrina and Rita had a devastating impact on the already stressed
ecosystem of the delta. USGS (2006) estimated 118 square miles of coastal wetlands had
been converted to open waters between the fall of 2004 and the fall of 2005. Hurricanes
Katrina and Rita not only impacted the already stressed delta ecosystem, but also stressed
the human resources to respond to the disaster
Wetland systems, including the Mississippi Delta, are some of the most productive
ecosystems in the world. The natural capital of wetland ecosystem services, the benefit
that humans directly or indirectly have with ecosystems, had an estimated value of
between $330 billion to $1.3 trillion/year in 2010 (Costanza et al., 1987; Batker, 2010).
This figure can be accounted for in the ecosystem services of nutrient cycles, water
purification, shoreline stabilization and protection, biodiversity, cultural, and
groundwater replenishment (Ramsar, 2010).
The Mississippi delta is degrading at an estimated 25-35 square miles per year
(USGS, 2010). This degradation associated with flood control measures (levees) and the
initial pursuit of economic stability in the region has subsequently backfired and has
stressed the ecosystems and ecosystem services, thus limiting the major opportunities for
economic stability to just several industries, those most prominent being oil and gas,
tourism, and fisheries. Should an incident occur that affects these industries, like the BP
Deepwater Horizon Oil Leak, the economic impact on the region is significantly greater.
32
It impacts livelihoods, lead to a decrease in quality of life, and limit the scope of
economic stability.
An additional factor impacting coastal Louisiana is the hypoxic zone in the Gulf of
Mexico the size ofNew Jersey and Delaware (Roach, 2005). A hypoxic zone in an
aquatic environment has less than 2mg/l of dissolved oxygen, creating an environment
which only microorganisms can survive. The hypoxic zone in the Gulf of Mexico is a
result of eutrophic conditions created by excess nutrients flow (nitrogen and phosphorus)
in the Mississippi river resulting from Midwestern agriculture.
2.3 Gulf Coast Livelihood
2.3.1 Regional Economic Stability
The Gulf of Mexico provides an estimated $234 billion in economic benefits to the
United States and Mexico. Among the largest contributors to economic productivity in
the region are the oil industry, tourism, and fisheries (Cato et.al., 2007). The lack of
economic diversity, due to the degrading delta, further exacerbates the economic and
social impacts associated with an oil disaster of this magnitude as well as the
vulnerability of the Louisiana coastal communities.
Industry'
On May 30, 2010, the Department ofthe Interior implemented a six-month
moratorium on deep water drilling after its initial assessment of the magnitude of the
Deep Water Horizon, set to expire November 30, 2010 (Department of Interior, 2010).
The purpose of the moratorium directive was to ensure industry has proven to the federal
33
government that the techniques and equipment used in deepwater drilling is safe. The
Bureau of Ocean Energy Management, Regulation, and Enforcement (formally Minerals
Management Service), the government branch that is responsible for regulating off shore
oil drilling, is in the process of revising its standards, methods, and means for regulating
and providing oversight of the offshore drilling industry. The directive, 30 C.P.R.
250.172(b ), states that under current conditions, "deepwater drilling poses an
unacceptable threat of serious and irreparable harm or damage to wildlife and the marine,
coastal and human environment"; 30 C.P.R. 250.172(c) addresses the need to install
"additional safety or environmental protection equipment is necessary to prevent injury
or loss of life and damage to property and the environment" (Department of Interior Press
Release, 2010). The economic concerns associated with this directive were voiced by
many ofLouisiana's elected officials. The moratorium was estimated to cause
temporarily unemployment to 8,000 deepwater platform workers and 23,000 people that
work in support of deep water drilling operations (NOLA.com, 2010). The oil industry in
the Gulf of Mexico is valued at $124 billion annually (Cato, 2009).
The degrading Mississippi delta once provided more diverse economic opportunities
for the delta communities' livelihoods in forms of farming, ranching, and subsistence
fishing. However, following extensive degradation, these communities have become
more dependent on the fisheries, tourism, and the oil and gas industry. This decrease in
options for sustainable livelihoods can be described as a lack of resilience. Due to the
lack of resilience and the few viable options for livelihood the economic impact of this
oil leak is anticipated to be large. It is too early to make an accurate assessment of the
impact that the oil incident will have on the gulf coast and in particular Louisiana. The
34
following economic overviews are to provide insight to the make-up of the Gulf Coast
economy.
Fisheries
The Gulf of Mexico is a primary source for seafood throughout the United States. In
2008 there were approximately 1.3 billion pounds of seafood coming from commercial
fishing and shellfish cultivation in the Gulf of Mexico valued at $661 million (National
Marine Fisheries Service, 2008). The fisheries sector comprises primarily of shrimp and
oysters, with Louisiana having the largest stake of the industry at 41% of the oyster and
finfish revenue and 47% ofthe shrimp revenue in the Gulf(EPA Gulf facts, 2010). On
May 2, 2010 the Federal government imposed an initial closure to fishing activities to an
area of 6,817 square miles. As the oil continued to leak, the closures were expanded,
peaking to their largest area of88,522 square miles on June 2, 2010. As of October 15,
2010 the closure remained in place for 16,481 square miles. The presence of oil found in
blue crab larvae remains a concern for the fisheries. Blue crab larvae, as well as other
planktonic species, play a vital role in the food web. The long-term ecological impact of
oil on the food chain species and species used for human consumption have not been
assessed in the event the contamination continues.
Tourism
Though difficult to quantify, the tourist industry in the Gulf of Mexico states (Texas,
Louisiana, Mississippi, Georgia, and Florida) is an estimated $100 billion industry (Cato,
2009). This includes hotels, restaurants, and those businesses dependant on automobile
and airplane traffic. Florida leads the Gulf States with their tourist economy equating to
35
60% of the total national tourist industry. Though specific numbers on those affected by
the spill are yet to be fully accounted, there is wide spread consensus that public fears
over the oil disaster has dramatically affected the Gulf of Mexico tourist industry.
Recreation
The gulf coast region accounts for nearly 42% (35,354,000 total trips in 2001) of all
marine sport fishing in the United States (Adams, et al. 2004). This provides benefit to
the coastal economy through employment of guides, boats, boat maintenance, and sport
fishing supplies and gear. The marine sport fishing industry accounts for $1.8 billion
annually and provides an estimated 56,000 jobs to the region (Adams et.al., 2004). Issues
of concern to this industry are the impact of oil toxicity to the gulf ecosystem, the
industrial and recreational fishing bans on gulf waters, and the impact of the media
broadcasts on perception of the sport fisherman.
Shipping
The shipping industry in the Gulf of Mexico accounts for $129 billion for Gulf of
Mexico states (Adams, et.al., 2004). This industry is not anticipated to have substantial
negative impacts by the oil spill. Precautionary measures such as monitoring ships
entering port areas for oil contamination are being taken at the mouth of the Mississippi
River (Reuters, 2010).
36
Environment:
Prior to the BP oil leak the Gulf of Mexico was an already stressed ecosystem due to
the extensive levee system, oil and gas industry, fisheries, and excess nutrient loads from
the Mississippi River which, through eutrophication, have depleted the dissolved oxygen
content and created a dead zone approximately the size of the state ofNew Jersey
(Roach, 2005). The anticipated environmental and ecological fallout from the
approximate 4.9 million barrels of oil leaked into the Gulf ofMexico as a result of the BP
Deep Water Horizon is great.
Initial samples of blue crab larvae found in the Gulf of Mexico indicated the presence
of oil (New York Times, 9112110). Although the crab larvae withstood the toxicity of the
oil contamination, it is unknown ifthe molting cycle of the blue crab will lead to the
elimination of oil from the larvae. If this does not occur, there is concern of
bioaccumulation of oil throughout the food chain leading to high concentrations of
hydrocarbons found in fish and marine mammals at the top of the food chain. At the time
of this writing, further research assessing impacts of the oil on blue crab larvae had not
been completed due to financial restrictions (New York Times, 9/12/2010). The presence
of oil in the blue crab larvae is significant from two points. Blue crab larvae are
planktonic and plankton serves as the basis of the food web. Though the long term
ecological impacts of these findings are unknown, there are concerns that this could lead
to a collapse in species counts ofblue crabs. It is unknown of the impacts on the greater
food web should this occur; though concerns remain that there will be a decrease in other
populations of marine species (Grey, 2010). Further, there are concerns of oil
contamination in other larval species, such as shrimp, potentially leading to further
37
ecological impact. Should these concerns come to fruition, there is a potential of collapse
of fisheries. The impact of oil contamination found in blue crab larvae on the food web
and ecological services will be assessed in future research.
Coral reefs are known as one of the most biologically diverse and ecologically
productive habitats on earth. They serve as primary producers in the food web and
provide a habitat for nearly 25% of marine species (Mullhall, 2007). According to the
World Wildlife Fund (20 11 ), if properly managed, reefs can produce 15 tons of fish per
square kilometer per year. The Gulf of Mexico coral reef system is no exception. A
NOAA led research expedition seven miles southwest of the Deepwater Horizon leak
revealed large scale coral die off at 4,500 feet (Rudolph, 201 0). The long-term impacts
of the coral reef death has not been fully assessed, however concerns persist of the
impacts on ecosystem services, the food web, deepwater habitats (NOAA, 2010).
The oil released following the Deep Water Horizon blowout introduced a massive
quantity of organic material (oil) into the Gulf. The process of degradation of organic
materials in a body of water requires the presence of oxygen and microorganisms. As the
degradation process occurs, the level of oxygen in the water decreases. Should dissolved
oxygen levels drop below 2 mg/L, hypoxic conditions will be present. Hypoxic
conditions occur in aquatic ecosystem deficient in dissolved oxygen, representing an
unhealthy ecosystem that does not sustain aquatic life beyond microorganisms. A healthy
aquatic ecosystem contains dissolved oxygen levels between 6-8 mg/L. Following the
BP Deep Water Horizon oil spill and blowout, Environmental Protection Agency (EPA)
and the National Ocean and Atmospheric Administration (NOAA) have monitored the
38
dissolved oxygen levels closely. They have reported an approximate 20% decrease in
dissolved oxygen levels from pre oil spill levels (NOAA Joint Analysis Group, 2010).
Environmental concerns regarding the oil spill and plant life in the coastal regions of
the Gulf remains a serious issue. A primary concern relates to contamination of the
wetlands soil resulting in the destruction of rhizomes of plant species (Jarvis, 2010).
Loss of plant density and species will lead to continued erosion of the wetlands. This is
significant from an ecological and disaster management perspective as the wetlands serve
as a filtration system and hurricane barrier to the coastal region of the Gulf. Further, 97%
of the species that are consider economically significant by the fisheries use the wetlands
at some point in their life cycle (Corn, 2010). Habitat destruction would likely lead to a
decrease in population counts in these species.
The long-term effects of oil spills on wetlands have not been thoroughly studied. A
study conducted by the Mineral Mining Service indicated that following a 1985 pipeline
leak of 300 barrels of oil, limited long term effects were noticed in the wetland system.
The re-growth of plant species was observed four years following the spill (Mendelssohn,
et.al., 1993). The chart below indicates the toll on wildlife in terms of contaminated
species, death of species, and number of species released into the wild following clean-up
as of October, 2010.
39
Species Collected Alive Collected Dead Released
Birds 2,080 6,104 1,245
Mammals 9 97 3
Sea Turtles 535 605 362
Other Reptiles 2 1 0
(USFWS/NOAA, 10114/2010)
2.3.4 Societal Issues:
Over the past 50 years there have been 400 oil spills worldwide and only seven
epidemiologic studies have been conducted following the spills (Slomski, 2010). The
Exxon Valdez spill provides the most thorough assessment of acute toxicity studies
resulting from an oil disaster. It was noted that the acute toxicity symptoms shown during
the Exxon Valdez spill were dizziness, nausea, vomiting, and sensitization of the skin
(Aguilera, et al., 2010). Those showing symptoms of acute toxicity were workers
involved with the cleanup efforts that resulted in their indirect exposure to the oil
(Aguilera, et al., 2010). Studies reviewing the Exxon Valdez spill in 1989 indicated the
major resulting health effect from the disaster were mental health symptoms. A study
completed in the years following the Alaska spill indicated an increase of Post Traumatic
Stress Disorder (PTSD) by 25% in native Alaskan communities that participated in
cleanup efforts and were dependent on fishing for their livelihood (Palinkas, et.al., 2004).
The land lost associated with the continual degradation, in the Mississippi Delta, has
resulted in communities that once had options to sustain their livelihood in forms of
ranching, hunting, agriculture, and fishing relying heavily on the fisheries to sustain their
livelihood. In instances where there is such dependence on limited sources of viable
40
economic stability, a disaster such as the BP oil leak has traumatic effects. As concerns
of a collapse of the fisheries for years to come persists (Schleifstein, 201 0), there is an
increased concern that members of the fishing communities will leave the region in
search of jobs elsewhere. This will deplete the social capital that has been established
over generations, ultimately leading to a loss of culture.
Following the oil leak, the fishing ban and the subsequent moratorium on deep water
oil drilling underscored the regional dependence on oil and gas, the fisheries, and tourism
for the populations' livelihoods as well as the societal vulnerabilities in circumstances of
an oil disaster. Many communities had completely lost their livelihood in a matter of
days. An example ofthis loss of livelihood was seen through Dean Blanchard Seafood
Inc. This firm accounted for 11% of the shrimp supply for the United States
(Goldenberg, 2010). Following the BP oil spill and the associated fishing ban, the
company had to assist in the clean-up operations as means to keep employees paid
(Goldenberg, 2010). Should the worst-case scenario come to fruition with the fisheries,
that being a collapse of the industry, companies like Dean Blanchard Seafood Inc and its
6,000 employees would lose their livelihoods. This scenario would further limit the
primary regional economy to oil and gas and tourism, extending further vulnerability and
decrease in resilience capacity.
. 41
2.4 Disaster Management:
Disaster management is the field that focuses on the management of events that cause
damage, loss, and/or destruction. In the United States it has recently become a more
prominent issue since the attacks on New York and Washington DC in September of
2001 and the aftermath of Hurricane Katrina in 2005.
2.4.1 Disaster Management Model
There are four phases to the disaster management paradigm. They are mitigation,
preparedness, response, recovery (Blanchard, 2008).
Mitigation in disaster management focuses efforts in avoiding any unnecessary
outcomes associated with disasters and includes legislation like building codes and
proper urban planning (FEMA, 2003).
Preparedness in disaster I11anagement includes the development of a preparedness
plan to minimize the effects of a disaster and to ensure that proper infrastructure and
logistical capacity is in place or can be implemented in the event of a disaster. Response
in disaster management is the implementation of the preparedness plan. The fmal stage
in the disaster management paradigm is the recovery stage. This phase can take years to
complete depending on the severity of the disaster as well as socioeconomic and political
factors (FEMA, 2003).
Following the Deepwater Horizon oil spill, the Department of the Interior established
the Strategic Sciences Working Group (SSWG). The objective ofthe SSWG was to
identify potential economic, environmental, and social impacts of the oil spill on the Gulf
42
Coast communities. As a result of SSWG meetings, the group created impact and
management phases following an oil spill. They were the emergency phase, the
restoration phase, and the reconstruction phase (Machlis, 201 0). The proposed research
will create a list of prioritized resilience measure to be implemented in St. Bernard
Parish that promote resilience against future oil related incidents in the Gulf of Mexico.
2.5 Application of Multi Criteria Decision Analysis to Resource Allocation and Oil
Related Incidents:
The inconsistencies found in BP's Gulf of Mexico regional oil spill plan further
underscores the importance of creating a new preparedness plan for oil related operations
in the Gulf of Mexico. BP's Gulf of Mexico regional oil spill plan was heavily criticized
as being insufficient and lacking useful information. Examples of inaccuracies included
citing wildlife protection of species not found in the Gulf of Mexico (walruses and sea
lions) and thoughtless estimates of worst case scenarios (National Commission on the BP
Deep Water Horizon Oil Spill and Offshore Drilling, 2011, New York Times, 6/14/201 0;
The Economist, 6/17/201 0; BP Gulf Oil Spill Response, 2009). This disaster illustrated
the need to create a practical response plan following an oil related incident in the Gulf of
Mexico. A component of this plan should address resource allocation following the
disaster.
These scenarios present varying impact assessments and intervention points following
the BP oil spill, however do not give recommendations on priority in addressing the
issues. As of this writing SSWG has not produced further documentation beyond the
scenario building associated with the BP Deepwater Horizon incident.
43
Multi Criteria Decision Models and particularly the Simple Multi-Attribute Rating
Technique (SMART) can be a useful tool as part of a disaster preparedness plan. This
model will provide an assessment of expert values and perception as to the priorities of
various resilience measures to be implemented in St. Bernard Parish that promote
resilience against future oil related incidents in the Gulf of Mexico. The constructed
prioritized list resulting from the SMART model will be useful to decision makers as
funds released following the oil spill will require a quick and informed response that
appropriately, efficiently, and effectively manages and mitigates the disaster.
2.5.1 Analytic Network Process (ANP):
A Multi-Criteria Decision Analysis using the Analytic Network Process (ANP) was
conducted during the time that the BP Deep Water Horizon oil spill was occurring. The
assessment by Levy & Gopalakrishnan was published online July 15,2010. ANP is
considered a broad version of the AHP (Saaty, 1999). The focus of this ANP was
resource allocation during BP's Deep Water Horizon oil spill and impact on the Gulf
coast. Their assessment included four objectives: higher economic payments to coastal
business; increase use of dispersants (meaning less oil will get to the shore); berm
construction to prevent oil from getting to the shore; and rebuilding Gulf Coast wetlands,
strengthen regulations, and promote sustainability and resilience (Levy, 2010). Details of
the experts' professional affiliations and the sample size used for the model were not
given. The completed rankings from the model can be seen in the following chart:
44
Alternatives Rankings Berm construction only 0.385 Rebuilding Gulf Coast wetlands, strengthen 0.304 regulations, promote sustainability and resilience Increase use of dispersants 0.196 Higher economic payments 0.115
Levy & Gopalakrishnan research is an assessment on allocating funds during the
emergency response phase for the BP Deepwater Horizon incident and assesses four
broad measures.
2.5.2 SMART and Resource Allocation:
In comparison to the previously mentioned decision model, the research conducted in
this dissertation uses a different methodology, assess the long-term resilience of St.
Bernard Parish, LA (opposed to emergency response), and provides a more
comprehensive assessment of resilience issues facing the coastal region.
The results of this research will be used in the development of a practical tool and
resource for decision makers to reference in the face of a future oil related incident in the
Gulf of Mexico. This tool can be integrated into the development of a disaster
preparedness plan.
The primary source of data for the research project used experts' experiences
associated with the BP oil leak. The timing of the data collection is after the emergency
response and the permanent capping of the oil well that occurred in September 2010.
Due to the length of time from the oil leak and capping of the well to the time this study
was conducted, the experts and their perceptions and values of resource allocation
45
following the oil incident may have changed values since the emergency response phase.
46
CHAPTER Ill
RESEARCH METHODOLOGY
The created decision model serves as an approach towards resource allocation using
financial penalties associated with the Deepwater Horizon oil leak and promotes
preparedness in St. Bernard in the event of a future oil related incident in the Gulf of
Mexico. The model creates a prioritized list of response steps to take following an
incident that will be useful to decision makers in the timely distribution of limited
resources for recovery efforts. It is important to note that this model is intended to
provide recommendations and guidance to decision makers in the face of future oil
related events in the Gulf of Mexico. The resource allocation model developed integrates
key experts perceptions and values. These values were assessed through an interview
process that asked experts from all expert groups to rank a number of pre-identified
objectives and sub-objectives as to their importance in relation to response activities.
Upon completion of the interview sessions, all results were statistically analyzed using
the Kruskal-Wallis H-test and Kendall's Coefficient of Concordance.
The creation ofthis model intends to assess the value trade-offs of the experts in an
objective manner. Each expert was assured anonymity throughout the completion of this
research in order to best obtain the value trade-offs of experts without restraint.
3.1 Development of the Model
The initial development and framing of the decision model began with a thorough
literature review of published articles involving oil spills resulting from deepwater oil
47
drilling incidents, decision modeling, Social Ecological Systems (SES' s ), and disaster
management. The literature review provided the researcher insight into the areas needed
for development of objectives and sub-objectives for the development of the decision
model.
The selection of the objectives and sub-objectives considered the criteria outlined in
Keeney & Raffia's 1992 book, Decisions with Multiple Objects. It is stated that the
decision objectives and sub-objectives should consider completeness, operational
function, absence of redundancy, succinctness and decomposability. Decomposability
refers to the ability to analyze an option by separating it into smaller components
(Hankins & Fos, 1989). The objectives support the overarching goal of the decision to be
made. This research will contain four objectives, Environment, Logistical Capacity for
Disaster Response, Regional Economic Stability, and Societal impacts. Sub-objectives
provide support and detail to each objective.
The model variables to be implemented into the SMART questionnaire for this
research were identified through use of the Delphi method. The Delphi method is a
process by which experts reach consensus answer a questionnaire to rate variables using
the Likert scale. This study used a modified Delphi method. Rather than giving an open
ended questionnaire in the first round, experts were asked to evaluate structured
statements developed through a focus group organized by the researcher. The objective
and sub-objectives results from the focus group can be found in Table 3-1. Following the
expert response, the questionnaires are gathered by the researcher and analyzed. The
researcher averages the responses given and calculates the standard deviation of each
variable. The results from all experts' responses from the first round are entered on the
48
questionnaire and given back to the experts for review. The experts assess the results
from their peers andre-rate each variable. This process is continued until consensus is
established. This study uses the rating of 3.5 on the Likert scale and a standard deviation
ofless than one as consensus. Below are the structured statements (sub-objectives)
identified by focus group to be rated in round one of modified Delphi method.
Environment:
Removal and disposal of residual oil: Allocate resources to cleaning oil that should surface, wash-up to shore, or invest in technology that best addresses underwater oil plumes.
Construct Oyster Reefs: Oyster reefs would promote shoreline stability and contribute to the ecosystem of Lake Borgne, St. Bernard Parish.
Cypress Swamp Restoration: Allocate funds to reroute the use of wastewater and freshwater to flow into the cypress swamps to mitigate the impacts of saltwater intrusion and promote cypress swamp restoration.
Prevention of Herbivory: Trapping of invasive species, primarily nutria, to reduce their impact on wetlands, therefore providing a healthier and more resilient wetland system. We have found that shootings are much more successful than trapping
Restoration of Barrier Islands: Infusion of sediment to counter the landmass lost to the barrier islands associated with erosion.
River Diversions: Use available funds for river diversions (Canarvon and Violet) in St. Bernard Parish to promote the introduction of freshwater to the marshes in St.· Bernard
Parish.
Sustainability of fisheries: Conduct studies quantifying the impact of the oil leak on fish populations and analyzing the long-term impact.
Logistical Capacity for Disaster for Response:
Basic Equipment and Necessities for residual oil clean-up: Boom, boats, dispersants, Personal Protective Equipment, food and shelter for workers, fuel.
49
Environmental Health Education and Training: HAZWOPER training, and other related training for programs to those needing to adjust their occupation to relevant and available work following an oil leak.
Establish monitoring system: Environmental Sensitivity Index, use of GPS, and methods that allow ease of reporting of oil sightings.
Information Sharing: Develop and implement systems for data integration, synthesis, sharing and dissemination.
Streamline Recovery Fund Procedure: Provide additional resources to community, particularly workers who can describe the paperwork that is required within applications.
Parish Staff I Parish Administrative capacity: Provide funds for additional St. Bernard Parish staff and administration to handle increase administrative needs associated with the BP Deepwater Horizon incident.
Funding for increased community meetings and programs: Promote understanding of all aspects of recovery and restoration of a disaster between expert groups.
Regional Economic Stability:
Economic Outreach Programs: Create television commercials and magazine adds to promote tourism.
Aquaculture Studies: Finance for independent laboratories to examine extent of contamination and bioabsorptionlbioaccumulation of aquaculture.
Restaurant and Hotel Subsidies: Subsidies for restaurant and hotel owners and workers to account for 100% of 5-year average profit prior to disaster.
Stipend for business adjustment to new environmental regulation: Provide money to small business that fund updating their business to meet new environmental regulation resulting from oil spill.
Loans for small business development: Increase availability of micro loans to individuals for small business use and/or start-up from affected communities.
Government funding: Provide local governments with funds to compensate reduced tax revenue as a result of reduced industry output.
so
Societal Impacts:
Community education outreach: Provide outreach sessions to communities that provide scientific backing on the health outcomes and effects of the oil and dispersants.
Bolster Educational Programs: Promote education and literacy in rural and urban regions.
Public health staffing and databases: Increase states resources for surveillance of adverse health effects associated with oil and dispersants.
Improve capacity of public health programs: Increase funds for social programs such as food stamps, school lunches, immunizations, and maternal health for effected populations.
Increase mental health care capacity: Develop resources and providers to deal with mental health problems, like Post Traumatic Stress Disorder (PTSD), to communities affected by the spill.
Population and demographic studies: Establish baseline data following the oil disaster to help determine future resource allocation.
Language translation: Provide language translation services to those in need.
3.2 Structuring the Decision Model
Consensus of model variables for the decision model was reached at the completion
of the second round of the Delphi method. The decision question was separated in a
decision tree. At the head of the decision tree was the title of the prioritized list of
resource allocation following an oil leak that promotes the long-term resilience of St.
Bernard Parish, LA. The problem was separated into four branches with the objective
titles. Each objective had corresponding branches for each sub-objective. The decision
tree for this research is found in Chapter 4, Figure 4-1
The objective categories and initial sub-objectives for the model were established
through the literature review in order to allow the model framework to have an emphasis
51
on social ecological systems. The researcher conducted a focus group session to refme
and modify the objectives and sub-objectives prior to the start of the Delphi rounds.
3.3 Survey Development:
The model variables identified through the Delphi method were used to develop a
survey that could used in order to determine the weights of the objectives and sub
objectives in the decision tree. The methodology used in development of the survey was
the Simple Multiattribute Rating Technique. The survey can be found in Appendix A.
3.3.1 Internal Review Board Approval:
The completed survey was submitted to the Internal Review Board (the IRB) at
Tulane University. The survey received expedited approval from the IRB on September
20, 2011 to conduct the proposed study with thirty participants. The request to omit
written consent was denied by the IRB. Prior to each interview and questionnaire session
the researcher discussed the goals of the study. The researcher answered any
questionnaires the participant had that pertained to the nature of the study goals as well as
questions regarding the collection of the data, written consent, and maintaining the
anonymity of all participants. The IRB approval can be found as Appendix B
3.3.2 Selection of Experts:
In order to make this decision model practical, effective, and useful, the data collected
for this research was to be of high quality. This was accomplished through the selection
of qualified experts. Those chosen to participate in the interview session were divided
into expe1i groups of scientific/technical, business representatives, government
52
representatives, and community based groups. For the purpose of weighting and
statistical analysis, each group had a minimum of six expert representatives from each
expert group. Participants were selected based on work experience in their given field
and direct working experience with the Deep Water Horizon oil incident. As an incident
of this nature can have detrimental effects on all facets of society, the research method
intends to capture the viewpoints and values of various stakeholder groups. An attempt
was made to gather equal number of representatives from each group.
The recruitment phase of the study was difficult and time consuming. The primary
mechanism used for the Delphi method was contacting the researchers' network of
professionals to gauge interest in participating in the study. Initial outreach for
participation in the Delphi method had varied success. A total of twenty-seven people
were contacted before the appropriate fit between the experts' background and experience
and a willingness to participate was found. The experts selected had a minimum of five
years experience in a resilience related field, extensive knowledge of issues facing
Southern Louisiana, and were impacted and/or had experience with the recovery process
following the BP Deepwater Horizon oil leak. The initial two rounds of this survey had
fifteen participants with a wide range of expertise.
Recruitment for participants in the SMART survey followed a similar methodology
of recruitment as the Delphi portion of the study. The participants of the Delphi method
were asked if they were willing to participate in the resource allocation instrument. Nine
of the fifteen participants agreed. Six participants either did not respond to the request or
replied that their schedules would not allow for further participation. Nine people
participated in the questionnaires and interviews. At the conclusion of each session, the
53
study participant was asked to recommend two to three people to participant for this
study. The recommended participants were contacted about their interest in participating
in the study. This method proved to be useful in fmding qualified participants.
Research on community groups that are active in St. Bernard Parish provided an
additional list of potential study participants. The researcher contacted the community
organizations to explain the study and gauge the level of interest in participating. This
resulted in varied success. Several groups were very interested and willing to participate,
while some groups never returned calls or emails. Selected participants in this portion of
the study had diverse backgrounds and a minimum of five years experience in the fields
of environment, fisheries, oil and gas, elected officials, government employees, biology,
chemistry, health sciences, non-profit organizations, community advocacy, and
economics and experience with the BP Deepwater Horizon oil leak. The SMART study
participants were placed in four expert groups. As previously mentioned, data was
collected in two phases: the Delphi method and the SMART interview and questionnaire
session. Data collection for the Delhi portion of the study took approximately eight
weeks from July 12, 2011 to September 14, 2011. The data collection phase for the
SMART interview and questionnaire took approximately seven weeks from September
20, 2011 to November 7, 2011. In general, study participants were met on an individual
basis. The length of the interview and questionnaire session ranged from forty-five
minutes to two hours. The data collection (Delphi and interview and questionnaire)
process was consistent with all participants.
54
Study participants had a diverse background and represented the following entities:
Tulane University School of Public Health
Sewerage and Water Board of New Orleans
City ofNew Orleans Department of Homeland Security
Tulane University Environmental Law Clinic
Southeast Louisiana Association of Contingency Planners
St. Bernard Economic Development Foundation
St. Bernard Parish Government
AVODAH
Lake Pontchartrain Basin Foundation
United States Army Corps of Engineers
Louisiana State University School of Coast and Environment
St. Bernard Community Center
Gulf Environment Associates
American Red Cross
In addition to listed firms and organizations above, four consulting firms that assist
the seafood industry and oil and gas requested their organization name not be included in
the study.
3.4 Data Collection:
As previously mentioned, data was collected in two phases: the Delphi method and
the SMART interview and questionnaire session. Data collection for the Delhi portion of
the study required approximately eight weeks from July 12,2011 to September 14, 2011.
The data collection phase for the SMART interview and questionnaire required
55
approximately seven weeks from September 20, 2011 to November 7, 2011. In general,
study participants were met on an individual basis. The length of the interview and
questionnaire session ranged from forty-five minutes to two hours. The data collection
(Delphi and interview and questionnaire) process was consistent with all participants.
The interviews and questionnaires were primarily conducted at the offices of the selected
participants. However, several study participants selected to conduct the sessions at a
cafe or restaurant of their choice.
3.4.1 Weighting the Variables:
The interview and questionnaire stage of the study asked experts representing four
expert groups to rank the objectives according to importance in response to resource
allocation following a large scale oil release event to promote the long-term resilience of
St. Bernard Parish, LA. The SMART methodology of variable weighting has been used
in multiple fields and established as a valid form of decision modeling. The initial
assumption of the ranking process is that all objectives are in an equal and worst state.
The objective perceived as the most important by the expert will be given the rank of one;
the objective perceived second most important will be given a rank of two. This process
will continue for all objectives to complete the ranking process. At the completion of the
initial ranking, the experts will then be asked to rank the objectives in a fashion that
establishes magnitude between each objective (Fos & Zuniga, 1999). This weighting
process is referred to as direct ranking. For example, if the economic stability objective is
ranked lowest in the first portion of the interview, it will be given a value of 10. If the
second least important objective is perceived by the experts as being 10 times more
important the objective will be given a ranking of 100. All objectives will be ranked
56
against the preceding objective. Following the ranking of importance, the weights will be
normalized by the equation described in Fos and Zuniga, 1999 below:
w;= normalized weight
Wi' =individual rating by expert
Wi' Wi=---n
.Lwi' i=l
2: Wi '= Sum of all individual ratings by experts
n = the number of variables
Following the initial steps of ranking the objectives, the same process was completed
for sub objectives within each objective category. After calculation of the raw values,
model variables of all participants were averaged before attaining the trade-off score.
The trade-off score were attained by multiplying the normalized weight of each sub
objective by the normalized score of its relevant objective (Fos and Zuniga, 1999 &
Alemi, 2007). The equation to accomplish this is below:
n
V. = ~w.v. J LJ l l
i=l
Yj= The overall score for the j alternative
W F Each individual variable normalized weight
Vi = Each individual variable value
n =The number ofvariables
57
The result of this method creates the prioritized list of resource allocation following a
large scale oil related event promoting long-term resilience in St. Bernard Parish, LA.
3.5 Data Analysis:
Kendall's Coefficient for Concordance and the Kruskal-Wallis H-test were used in
this research to determine statistical relationship between the expert groups. The results
of the statistical analysis will provide insight to the views and values found among and
within study participants.
3.5.1 Kendall's Coefficient for Concordance:
Kendall's Coefficient for Concordance is a statistical analysis that measures the
strength of agreement in a given situation (Landis & Koch, 1977). This form of analysis
is useful in identifying correlations in prioritizing objects according to values, judgment,
and perceived importance (Jirapongsuwan, 2008). Kendall's Coefficient for Concordance
has a range of 0 to 1. A score of 0 represents no agreement, while a score of 1 equals
total agreement among study participants (Robinson, 1957). The null hypothesis in this
research will support no correlation between the rankings of experts, while the alternative
hypothesis will support a correlation between experts.
Hypothesis 1: There is a correlation between all expert participants in variable ranking
Hypothesis 2: There is a correlation within the Government Representative expert group in variable ranking
Hypothesis 3: There is a correlation within the Impacted Business expert group in variable ranking
Hypothesis 4: There is a correlation within the Community Based expert group in variable ranking
58
Hypothesis 5: There is a correlation within the Science and Technical expert group in variable ranking
Hypothesis 6: There is a correlation within the experts selected to participated in the Delphi method (defining model variables) and the SMART questionnaire
Hypothesis 7: There is a correlation among the experts who participated in the SMART questionnaire
3.5.2 Kruskal-Wallis H-test:
The Kruskal-Wallis H-test is accepted as the non parametric version of the parametric
One-Way Analysis of Variance and serves as a form of analysis that statistically assesses
the difference between independent groupings (Corder & Foreman, 2009). By using the
ranks, opposed to the raw data, the Kruskal-Wallis H-test will be used to analyze the
associations of ranking of model variables from different grouping of study participants.
The null hypothesis identifies no difference in the median value between the identified
expert groups, and the alternative hypothesis identifies a difference in median values for
between the expert groups. In order to reject the null hypothesis the H-value must be
greater than the critical value (Siegal &Castellan, 1988). The H-value is obtained from
the Chi-square table and using degrees of freedom (k-1), (Jirapongsuwan, 2008; Siegal
&Castellan, 1988). The following hypotheses were tested using the Kruskal-Wallis H-
test in this study.
Hypothesis 8: There is an agreement in ranking of model variables in the four expert groups
Hypothesis 9: There is agreement in ranking of model variables between the expert participants (those who completed the Delphi method and the SMART questionnaire) and the experts (those who completed the SMART questionnaire).
59
CHAPTER IV
RESEARCH RESULTS
This chapter will provide results from the study, including the response rate, the
response from the Delphi portion of the study, results from the SMART questionnaire,
and the results from Kendall's Coefficient of Concordance and the Kruskal-Wallis H test.
The model variables will then be sorted from highest weight to the lowest weight,
creating the prioritized list of resource allocation. The cumulative percent model was
utilized to provide additional organization to the list and to identify which model
variables should be included in the face of limited financial resources.
4.1 Response Rate
The initial phase of this research was to identify pertinent variables for the study.
Fifteen experts were recruited to defme the decision model variables through use of the
Delphi method. Consensus on twenty-nine model variables was attained at the
completion of the second round. Attrition was not an issue during this portion of data
collection. The study anticipated using the fifteen experts who participated in the Delphi
method to complete the SMART interview and questionnaire phase of the study.
However six participants chose not to complete the study. Three of the six participants
who declined to complete the SMART interview and questionnaire expressed lack of
time to continue participation in the study. The remaining three participants failed to
respond to the researcher's repeated attempts to schedule time for the SMART interview
and questionnaire.
60
Thirty experts participated in the SMART interview and questionnaire phase of this
study. The participants included the nine experts who expressed a willingness to
continue participation following the Delphi portion of the study.
4.2 Delphi Method Results:
Two rounds of the Delphi were completed before consensus was reached on model
variables for the study.
4.2.1 Round 1 Results:
The initial round of the study asked fifteen experts to rate variables to the importance
of resource allocation that promotes the long term resilience of St. Bernard Parish
following the BP Deepwater Horizon incident. The established inclusion criteria for the
Delphi method in this study were variables that rated both with a mean of 3.5 or above
and a standard deviation less than one. This level was established from the literature
review and revised due to the anticipated diverse views and interests among study
participants. The variables were categorized into four objectives: Environment,
Logistical capacity for disaster response, Economic Stability, and Societal impacts. First
round results can be found in Table 4-1.
Table 4-1: Round 1 results ofthe Delphi method
Environmental Sub-objectives
Variables Mean
Removal and Disposal of Residual Oil 4.16
Construction of Oyster Reefs 4.20
61
Standard Deviation
0.937
0.861
River Diversions 4.46 0.967
Cypress Swamp Restoration 4.73 0.457
Herbivory Prevention 3.86 0.862
Restoration of Barrier Islands 4.33 0.975
Examine Sustainability ofFisheries 4.40 0.828
Additional Variables Suggested by - -
Experts
Funds for Research Impacts of Rising Energy Costs
Funds for Researching Impact of Climate Change
Monitoring natural attenuation of residual oil
Logistical Capacity for Disaster Response
Variables Mean Standard Deviation
Basic Equipment and Necessities for Residual Oil Clean-Up 3.86 1.060
Education and Training 4.13 0.833
Establish Monitoring System 4.06 0.883
Information Sharing 4.33 0.816
Streamline Recovery Fund Procedure 4.46 0.767
Funds for Increased Community Meetings and Programs 4.00 0.925
Parish Administrative Capacity 3.60 0.910
62
~----~~-- ~~ ~ --- ~--- -~-~~~-· ·--- -~~ --- --- ----~·~--- -- ~~~~------------~--- -------------~ --~--~--------- ~- -----~--~--· - ~ ---- ------------
Economic Stability
Variables Mean Standard Deviation
Outreach Programs 4.00 1.000
Aquaculture Studies 3.80 0.941
Restaurant and Hotel Subsidies 2.93 0.798
Stipend for Business Adjustment to New Environmental Regulation 3.86 0.915
Loans for Small Business Development 4.33 0.899
Funding for Government Services 3.46 1.06
Additional Variables Suggested by Experts
Oversight of the distribution and use of funds
Economic development plan to enhance and build additional business opportunities
Societal Impacts Variables:
Variables Mean Standard Deviation
Community Education Outreach 4.40 0.828
Bolster Educational Programs 4.40 0.632
Public Health Staffmg and Databases 3.86 0.833
Improve Capacity ofPublic Health Programs 4.06 0.798
Increase Mental health Capacity 4.13 0.639
Population and Demographic Studies 3.66 0.975
63
Language Translation 3.66 0.723
Additional Variables Suggested by Experts
Improve Medical Care Services
Train Healthcare Providers about Health Effects Related to Oil Spill
Two variables did not meet the criteria for inclusion as a SMART questionnaire
variable in the first round of the Delphi study. Both were sub-objectives under the
economic stability objective. The two variables, outreach programs and basic equipment
and necessities for residual oil removal and cleaning, had an average rating above 3.5 but
a standard deviation at or above one. All other variables for the first round met the
inclusion criteria. Funding for government services was very close to the inclusion
criteria with an average rate of 3.46 and a standard deviation of 1.06. The restaurant and
hotel subsidies variable had the lowest rating of all variables with a mean of 2.93 and a
standard deviation of 0. 798.
Additional variables suggested by the experts in Delphi Round 1 were identified in
the categories ofEnvironmental, Economic Stability, and Societal Impacts. Additional
variables suggested for the Environmental category included funds for research on
impacts of rising energy costs, funds for researching impacts of climate change, and
monitoring natural attenuation of oil. Additional variables suggested by experts in the
category of Economic Stability included oversight ofthe distribution and use of funds,
and economic development plan to enhance and build additional business opportunities.
64
Variables suggested by experts in the category of Societal Impacts included improve
medical care services, and train healthcare providers about health effects related to oil
spill. All additional model variables were added in the second Delphi round.
4.2.2 Round Two Results
The fifteen experts who participated in the first round of the Delphi study were asked
tore-rate the variables from round one with consideration to all experts' results. The
second round questionnaire was similar to the round one questionnaire, with exception of
the new recommended variables and the results (mean and standard deviation) of first
round added. The second round Delphi questionnaire can be found in Appendix D. The
results of the second round of the Delphi method can be found in Table 4-2.
Table 4-2: Round 2 results of the Delphi method:
Environmental Sub-objectives
Variables Mean Standard Deviation
Removal and Disposal of Residual Oil 4.14 0.947
Construction of Oyster Reefs 4.06 0.961
River Diversions 4.33 1.012
Cypress Swamp Restoration 4.80 0.414
Herbivory Prevention 3.80 0.987
Restoration of Barrier Islands 4.27 0.961
Examine Sustainability ofFisheries 4.33 0.816
Additional Variables Suggested by - -
Experts
3.20 1.014 Funds for Research Impacts of Rising
65
Energy Costs
Funds for Researching Impact of Climate 3.78 0.892 Change
Monitoring natural attenuation of residual 3.60 0.828 oil
Logistical Capacity for Disaster Response
Variables Mean Standard Deviation
Basic Equipment and Necessities for Residual Oil Clean-Up 3.73 1.090
Education and Training 4.20 0.774
Establish Monitoring System 4.13 0.990
Information Sharing 4.06 0.883
Streamline Recovery Fund Procedure 4.13 0.832
Funds for Increased Community Meetings and Programs 3.93 0.961
Parish Administrative Capacity 3.73 0.961
Economic Stability
Variables Mean Standard Deviation
Outreach Programs 4.06 0.961
Aquaculture Studies 3.80 0.861
Restaurant and Hotel Subsidies 2.86 0.743
Stipend for Business Adjustment to New Environmental Regulation 3.66 0.899
Loans for Small Business Development 4.26 0.883
66
-~--~- -·----~-------~-- -~~--- --~------· --
Funding for Government Services 3.60 0.986
Additional Variables Suggested by Experts
Oversight of the distribution and use of 3.20 1.146
funds
Economic development plan to enhance 3.00 1.253 and build additional business opportunities
Societal Impacts:
Variables Mean Standard Deviation
Community Education Outreach 4.46 0.743
Bolster Educational Programs 4.20 0.676
Public Health Staffing and Databases 3.73 0.961
Improve Capacity ofPublic Health Programs 4.33 0.617
Increase Mental health Capacity 4.26 0.703
Population and Demographic Studies 3.53 0.990
Language Translation 3.73 0.883
Additional Variables Suggested by Experts
Improve Medical Care Services 4.00 0.925
Train Healthcare Providers about Health 3.33 1.112
Effects Related to Oil Spill
Consensus was reached at the completion of second round of the Delphi method.
The variables that were not included as part of the study were: train healthcare providers
67
about health effects related to oil spill, oversight of the distribution and use of funds,
hotel and restaurant subsidies, and economic development plan to enhance and build
additional business opportunities. The mean rating of each was below the 3.5 threshold
for acceptance. It was mentioned to the researcher multiple times that the potential
variable of economic development plan to enhance and build additional business
opportunities was redundant to the loans for small business development. The first
research question documented in Chapter 1 was answered using the Delphi methodology.
Question: What are the model variables to be addressed in this multi-criteria value
model that promotes long-term resilience with consideration to future oil related
events in St. Bernard Parish, LA?
The Environmental objectives are removal of and disposal of residual oil, Cypress
swamp restoration, construction of oyster reefs, river diversions, restoration of barrier
islands, herbivory prevention, examine sustainability of fisheries, research impacts of
climate change, and monitoring natural attenuation of residual oil.
The Logistical Capacity for disaster response sub-objectives are basic equipment and
necessities to clean residual oil, establish monitoring systems, information sharing,
streamline recovery funds, funds for increased community meetings and programs, parish
administrative capacity.
The Economic Stability sub-objectives are funding for government, outreach
programs that promote tourism and safety of fisheries, aquaculture studies, stipend for
adjustment to new environmental regulation, and loans for small business development.
68
The Societal Impact sub-objectives were identified as community outreach and
support programs, bolster education programs, public health staffmg and surveillance
database, improve capacity for public health programs, increase mental health capacity,
population and demographic studies, language translation, improve medical care services.
The identified model variables will be considered by experts in creating the prioritized
list of resilience measures for St. Bernard Parish with consideration to future oil related
incidents. With the model variables defined, the problem was structured in a value tree
as mentioned in Chapter 3. The value tree can be found in Figure 4-1.
69
Figure 4.1
Value Tree
GOAL OBJECTIVE SUB-OBJECTIVE
Construction of Oyster Reefs
River Diversions
Cypress Swamp Restoration
Herbivory Prevention
Environment Removal and Disposal of Residual Oil
Restoration of Barrier Islands
Examine Sustainability of Fisheries
Funds Researching Impacts of Climate Change
Monitoring Natural Attenuation of Oil
Basic Equipment and Necessities for Residual Oil Removal
Education and Training
Establish Monitoring System
Logistical Capacity Information Sharing
Streamline Recovery Fund Procedure
Resource Allocation Funds for Increased Community Meetings and Programs
Parish Administrative Capacity
Outreach Program
Aquaculture Studies
Economic Stability Stipend for Business Adjustment to New Environmental Regulation
Loans for Small Business Development
Funding for Government Services
Community Outreach Program
Bolster Education Outreach
Public Health Staffing and Surveillance Database
Societal Impacts Improve Capacity of Public Health Programs
Increase Mental Health Capacity
Population and Demographic Studies
Language Translation
Improve Medical Services
70
4.3 Simple Multi-Attribute Rating Technique (SMART):
The SMART portion of the study was used to create a scoring and subsequent ranking
system for the model variables. The thirty identified experts were interviewed and asked
to complete the SMART questionnaire in sessions that lasted approximately forty-five
minutes to one hour each. Each expert was asked to rank and provide weights for the
model variables. The weights were normalized by using the sum of all weights for a
given objective category divided by the individual score. The sum of all normalized
weights equals 1. The resulting normalized weights were used in construction of the
value tree.
The value-tree provides an illustration of the variables and their normalized weight.
The frrst set of branches for the value tree represent the primary objectives of the decision
model, while the second set of branches represent the sub-objectives of the model. The
arithmetic mean of the experts' response is used for the weights of the individual
objective and sub-objective scores. The trade-off scores are obtained by multiplying a
sub-objective weighted score by its corresponding objective weighted score. For
example, the weight for the economic stability objective was 0.2744 and the loan for
small business development sub-objective had a weight of 0.3400, resulting in a trade-off
score of 0.0933. A larger trade off score indicates a higher value placed on a given
variable than a variable with a smaller trade off score. The value tree with the weighted
results can be found in Figure 4-2.
71
Figure 4-2
Value Tree and Trade-off Scores
GOAL OBJECTIVES SUB-OBJECTIVES TRADE-OFF SCORES
Corutmction of Oyster Reefs WEI =0.0878 WE*WEl = 0.0343
River Divenioru WF2 = 0.1283 WE*WFJ. = 0.0501
Cypress Swamp Restoration W8 = 0.2069 WE*WEl = 0.0808
Heroivozy Prevention WFA =0.0300 WE*WFA = 0.0117
l!nviromnental WE= 0.3904 Removal and Disposal of Residnal Oil WFS = 0.1965 WE*WFS = 0.0767
Restoration of Banier Islands WE6 = 0.1532 WE*WE6 = 0.0598
Examine Sustainability of Fisheries WEI= 0.0847 WE*WID = 0.0331
Funds Researching the Impacts of Climate Change WFJ,=0.0513 WE*WFJ, = 0.0200
Monitoring Natural AttelUiation of Oil W8 =0.0612 WE*Ws = 0.0239
Basic Equipment and Necessities for Residnal Oil Removal WDRl =0.1792 WDR*WDRl = 0.0237
Edncation and TRining WDR1=0.1417 WDR *WDR1 = 0.0187
Establish Monitoring System WDR3 =0.1630 WDR*WDR3 = 0.0215 Capacity WDR = 0.1322 Information Sharing WDR4 = 0.2298 WDR *WDR4 = 0.0304
Streamline Recovezy Fund Procednre WDR5 = 0.1534 WDR *WDR5 = 0.0203 Resource Allocation Funds for Increased Comnmnity Meet~ and Programs WDR6 = 0.0493 WDR *WDR6 = 0.0065
Parish Administrative Capacity WDR7 = 0.0836 WDR*WDR7 = 0.0111
Ou !reach Program WFSl =0.1694 WFS*WFSl = 0.0465
Aquaculture Studies WFS2 = 0.2097 WFS*WFS2 = 0.0575
Economic Stability WFB= 0.2744 Stipend for Business Adjustment to New Environmental Reg~ WJID = 0.1372 WFS*WFSJ = 0.0376
Loans for Small Business Development WFS4 = 0.3400 WFB*WFS4 = 0.0933
Funding for Govenunent Services WFSl = 0.1436 WFB * WFBl = 0.0394
Comnmnity Ou !reach Programs WSl =0.1655 Ws*WsJ = 0.0336
Bolster Elhication Programs W82 = 0.1002 Ws*Wsz = 0.0204
Public Health Staffing and Surveillance WSJ = 0.1200 W8 * WSJ = 0.0244
Societal Impacts W8=0.2301 Improve Capacity of Public Health Programs W84 = 0.1760 W8 * W84 = 0.0357
Increase Mental Health Capacity W85 = 0.1988 Ws*Wss = 0.0404
Population and Demographic Studies W86 =0.0488 Ws*Ws6 = 0.0099
Language Translation W87 =0.0233 Ws*Ws7 = 0.0047
Improve Medical Services W88 = 0.1671 Ws*Wss = 0.0340
72
4.4 Hypothesis Testing:
Kendall's Coefficient of Concordance (W) and the Kruskal-Wallis H test were used
to assess the correlation and level of agreement between the groups of experts and
answers research question two: What are the differences, values, and preferences
across and among the expert's identified expert groups?
4.4.1 Kendall's Coefficient of Concordance (W):
Kendall's Coefficient of Concordance (W) is a statistical method to analyze the level
of agreement among k experts in prioritizing n objectives based on the experts' views on
the relative importance of model variables. Kendall's Coefficient of Concordance (W)
was used to assess the agreement within each expert group as well as all experts placed in
one group. An additional analysis was conducted to assess the level of agreement within
those experts who completed the Delphi portion of this study and those experts who just
participated in the SMART portion of the study. The analysis assesses level of
agreement of sub-objective group, rather than individual attributes. The null hypothesis
indicates no correlation among rankings while the alternative hypothesis indicates
significant correlation between expert rankings. The hypothesis uses a 95% level of
confidence in this study.
Hypothesis 1: There is correlation among all expert participants in variable ranking.
Kendall's Coefficient of Concordance (W) was used to assess the level of agreement
of objectives and sub-objectives within the 30 experts who participated in the SMART
portion of the dissertation. With the agreement level established at 0.05, the results
73
indicate agreement among the experts significant. The objective group level of
agreement was low at W= 0.275. The level of agreement for the sub-objective groups
was moderate to low. Societal impacts sub-objective group had the highest level of
agreement (W= 0.325) followed by the environmental sub-objective group (W= 0.264),
and logistical capacity for disaster response sub-objective group (W=0.225). The
economic stability sub-objective group was found to have particularly low level of
agreement at 0.142. The results can be found in Table 4-3. There is agreement in model
variables among the thirty participants. The significance level ofless than 0.05 for all
variables leads to the rejection of the null hypothesis. The significance of agreement
among study participants indicate that the model variables identified by experts are valid
for the decision modeL The results can be found in Table 4-3.
Table 4-3:
k n df Kendall W Chi-Square p-value Objectives 30 4 3 0.275 24.720 0
Sub objectives Environment 30 9 8 0.264 63.400 0 Logistical 30 7 6 0.225 40.470 0 Economic 30 5 4 0.142 17.040 0.002 Societal 30 8 7 0.325 68.220 0
Hypothesis 2: There is a correlation within the Government representative expert group in variable rankings.
The Government Expert group was comprised of six participants and had a moderate
level of agreement in ranking variables. The highest level of agreement was the
environmental sub-objective group (W=0.588) followed by logistical capacity for disaster
74
response sub-objective group (W=0.484), economic stability sub-objective group
(W=0.422), and societal impacts sub-objective group (W= 0.341). The significance level
was established at less than 0.05, resulting in four of the five variables established at a
significant level of agreement. The objective variable at an agreement level of0.367 and
a p-value of0.086, however, did not. Four of five groups were significant therefore the
null hypothesis was rejected and a correlation between experts in this group was found.
The results of Kendall's Coefficient of Concordance (W) for the government
representative expert group can be found in Table 4-4.
Table 4-4:
k n df Kendall W Chi-Square p-value Objectives 6 4 3 0.367 6.600 0.086
Sub objectives Environment 6 9 8 0.588 28.220 0 Logistical 6 7 6 0.484 17.430 0.008 Economic 6 5 4 0.422 10.130 0.038 Societal 6 8 7 0.341 14.330 0.046
Hypothesis 3: There is a correlation within the impacted business expert group
There was moderate agreement within the impacted business group. Significance (p-
value less than 0.05) was found in all sub-objectives. The highest level of agreement was
found with the economic sub-objectives (W= 0.617) followed by logistical capacity for
disaster response sub-objectives (W= 0.530), environmental sub-objectives (W= 0.433),
and societal impacts sub-objectives (W=0.354). The objective variable had agreement
level of 0.344 however was not considered significant with a p-value at 0.1 02. Four of
75
five variables were significant; therefore the null hypothesis was rejected for four of five
variables. The results can be found in Table 4-5.
Table 4-5:
k n df Kendall W Chi-Square p-value Objectives 6 4 3 0.344 6.200 0.102
Sub objectives Environment 6 9 8 0.433 21.240 0.007 Logistical 6 7 6 0.530 19.070 0.004 Economic 6 5 4 0.617 14.800 0.005 Societal 6 8 7 0.354 14.890 0.037
Hypothesis 4: There is a correlation within the community based expert group
The community based experts level of agreement was significant (p-value less than
0.05) for all variables. Levels of agreement for sub-objectives were moderate to low with
the societal impact variable sub-objectives having the highest level of correlation (W=
0.424) followed by environmental sub-objectives (W= 0.413), economic stability sub
objectives (0.235), and logistical capacity for disaster response sub-objectives
(W=0.229). The objective variable had a level of agreement at 0.494 and a p-value of
0.001. The null hypothesis of no correlation can be rejected. The results can be found in
Table 4-6.
Table 4-6:
k n df Kendall W Chi-Sqliare p-value Objectives 11 4 3 0.494 16.310 0.001
Sub objectives Environment 11 9 8 0.413 36.320 0.000 Logistical 11 7 6 0.229 15.180 0.019 Economic 11 5 4 0.235 10.330 0.035 Societal 11 8 7 0.424 32.670 0.000
76
Hypothesis 5: There is correlation within the science and technical expert group
The Science and Technical expert group had a moderate to low level of agreement of
the sub-objective. The environmental sub-objectives had the highest level of correlation
(W= 0.434), followed by the logistical capacity for disaster response sub-objectives (W=
0.366), societal sub-objectives (W= 0.348), and economic stability sub-objectives with a
low level of agreement (W= 0.282). The objective variable had an agreement of .363 and
a p-value of 0.054. The objective variable and economic variable had p-values greater
than 0.05. The null hypothesis can be rejected for three of five variables. The results can
be found in Table 4-7.
Table 4-7:
Objectives
Sub objectives Environment Logistical Economic Societal
k 7
7 7 7 7
n 4
9 7 5 8
df Kendall W 3 0.363
8 6 4 7
0.434 0.366 0.282 0.348
Chi-Square p-value 7.629 0.054
24.31 15.37 7.870 17.05
0.002 0.018 0.096 0.017
Hypothesis 6: There is correlation within the expert group that defined model variables.
There were nine experts that participated in defining model variables and completed
the SMART questionnaire. The range of correlation between this group of experts was
0.426 to 0.178. The sub-objectives of logistical capacity for disaster response and
economic stability had particularly low correlation at 0.189 and 0.178 respectively and p-
values greater than 0.05. The results oflow levels of correlation between the previously
mentioned sub-objectives are a result of the diversity of study participants who identified
model variables. The researcher made an attempt to include as diverse of group as
77
possible to adequately incorporate the backgrounds of stakeholder groups within St.
Bernard Parish. More important, the p-values of the aforementioned sub-objectives were
substantially above the significance level of 0.05, thus indicating a lack of significance in
the rankings. As seen in Table 4-8, Lack of significance among the Logistical Capacity
and Economic Stability is accounted for based on the low sample size and the diversity in
view points of those study participants who completed both the Delphi and SMART
portions of the study. It is recommended that future updates to this study include a larger
sample size. The null hypothesis of no correlation can be rejected for three of five
variables.
Table 4-8:
k n df Kendall W Chi-Square p-value Objectives 9 4 3 0.338 9.133 0.028
Sub objectives Environment 9 9 8 0.414 29.807 0.000 Logistical 9 7 6 0.189 10.190 0.117 Economic 9 5 4 0.178 6.400 0.171 Societal 9 8 7 0.426 26.852 0.000
Hypothesis 7: There is a correlation within the experts not involved in defining model variables
The expert group that was comprised of those who did not participate in defining the
model variables (did not participate in the Delphi model) showed a low, but significant
correlation (p-values ofless than 0.05) for all model variables. The range of variable
agreement was between 0.308 and 0.146. The economic stability sub-objectives had the
lowest correlation. The null hypothesis of no correlation can be rejected for all variables
and the results can be found in Table 4-9.
78
Table 4-9:
k n df Kendall W Chi-Square p-value Objectives 21 4 3 0.260 16.370 0.001
Sub objectives Environment 21 9 8 0.292 49.000 0.000 Logistical 21 7 6 0.265 33.408 0.000 Economic 21 5 4 0.146 12.310 0.015 Societal 21 8 7 0.308 45.240 0.000
4.4.2 Kruskal-Wallis H-test:
The Kruskal-Wallis H test was used to measure the agreement of variable rankings by
identified expert groups as well as the agreement of median variable ranking between
those that identified model variables to those who did not. The null hypothesis
considers no difference in the median between the expert groups in the first analysis and
no difference between the study participants who identified model variables (participated
in the Delphi method) to those who did not. The alternative hypothesis considers a
difference in agreement in variable rank between identified expert groups. The Kruskal-
Wallis H-test hypothesis for this research used a significance level of95%. The results
can be found Tables 4-10 and 4-11.
Hypothesis 8: There is an agreement in ranking of model variables in the four expert groups
The Kruskal-Wallis H-test was used to analyze the compared median ranked
priorities of objectives and sub-objectives between expert groups. Significance level was
set 95%. Significance was found with eight variables, including the environmental
objective and five of the environmental sub-objectives. Additional variables found to be
significant were streamline recovery fund procedure, parish administrative capacity, and
79
funding for government. The null hypothesis considering agreement of all study
participants can be accepted for twenty-five of the thirty-three objectives and sub-
objective variables.
Table 4-10: Kruskal-Wallis H-test ofvariables for experts' agreement among the four
expert groups (df= 3):
Variable
Environmental Logistical Capacity for Disaster Response Regional Economic Stability Societal Impacts
Removal and Disposal of Residual Oil Cypress Swamp Restoration Construction of Oyster Reefs River Diversions Restoration of Barrier Islands Herbivory Prevention Examine Sustainability of Fisheries Researching Impacts of Climate Change Monitoring Natural Attenuation of Residual Oil
Basic Equipment for Residual Oil Clean Up Education and Training Establish Monitoring System Information Sharing Streamline Recovery Fund Procedure Funds for Increased Community Meetings/Programs Parish Administrative Capacity
Funding for Government Outreach Programs to Promote Tourism/Fisheries Aquaculture Studies Stipend for Business Adjustment to New Enviro Regulation Loans for Small Business Development
80
Chi Square p-value
12.580 0.006*
1.813 0.612 7.158 0.067 3.733 0.292
5.720 0.126
12.090 0.007*
6.120 0.106 9.560 0.023* 9.620 0.022* 7.490 0.058 4.810. 0.186
10.794 0.013* 7.480 0.058
2.730 0.434 4.660 0.199 1.550 0.672 4.152 0.246
13.270 0.004* 2.560 0.465 8.010 0.046
10.930 0.012* 3.880 0.275 7.430 0.059 7.560 0.056 3.860 0.277
Community Outreach and Support Programs 2.450 0.484 Bolster Education Programs 2.160 0.540 Public Health Staffmg and Surveillance Database 2.350 0.504 Improve Capacity for Public Health Programs 6.590 0.086 Increase Mental Health Capacity 4.060 0.254 Population and Demographic Studies 1.203 0.752 Language Translation 0.004 1.000 Improve Medical Services 0.566 0.904
Hypothesis 9: There is agreement in ranking model variables between those that defmed the model variables and those that did not.
The Kruskal-Wallis H-test was used to analyze the level of agreement between the
study participants who defmed model variables. The group that defined model variables
participated in the two rounds of the Delphi method and completed the SMART
interview and questionnaire session. The significance level was set at 0.05. The
comparison of median ranked priorities of objectives and sub-objectives between the two
groups indicate statistical significance for two sub-objectives within the environment
objective. Those are removal and disposal of residual oil and river diversions. The
results indicate acceptance of the null hypothesis of no difference in median ranking
between the two groups for thirty-one of the thirty-three model variables.
Table 4-11: Kruskal-Wallis H-test ofvariables for experts' agreement among two
groups, those that identified model variables and those who did not ( df = 1):
Objective Environmental
Chi-Square p-value
Logistical Capacity for Disaster Response Regional Economic Stability Societal Impacts
81
0.090 0.765 0.920
0.001 0.442
0.338
0.981 0.506
- - -------------
Removal and Disposal of Residual Oil 4.630 0.031 * Cypress Swamp Restoration 0.557 0.456 Construction of Oyster Reefs 0.042 0.837 River Diversions 9.087 0.003* Restoration of Barrier Islands 0.053 0.819 Herbivory Prevention 0.844 0.358 Examine Sustainability ofFisheries 1.408 0.235 Researching Impacts of Climate Change 0.089 0.765 Monitoring Natural Attenuation of Residual Oil 1.657 0.198
Basic Equipment for Residual Oil Clean Up 1.113 0.292 Education and Training 0.360 0.548 Establish Monitoring System 0.089 0.765 Information Sharing 0.237 0.627 Streamline Recovery Fund Procedure 0.511 0.475 Funds for Increased Community Meetings/Programs 0.378 0.538 Parish Administrative Capacity 0.009 0.925
Funding for Government 0.124 0.725 Outreach Programs to Promote Tourism/Fisheries 0.035 0.852 Aquaculture Studies 2.260 0.133 Stipend for Business Adjustment to New Enviro Regulation 0.199 0.656 Loans for Small Business Development 0.002 0.961
Community Outreach and Support Programs 0.967 0.325 Bolster Education Programs 0.432 0.511 Public Health Staffing and Surveillance Database 0.089 0.765 Improve Capacity for Public Health Programs 0.541 0.462 Increase Mental Health Capacity 0.174 0.677 Population and Demographic Studies 0.485 0.486 Language Translation 0.984 0.321 Improve Medical Services 1.860 0.173
4.4.3 Hypothesis Conclusion and Summary:
Significance was established for twenty-nine of thirty-five variables using Kendall's
Coefficient of Concordance (W). Significance establishes agreement among the experts
and their grouping. Levels of agreement among experts were stronger within their expert
82
------- ------------ .. -- -------- ----
group opposed to the collective group of experts. Results between the experts that
identified model variables and those that did not, found higher levels of agreement within
the group that identified the model variables, however those that did not identifY model
variables results were more consistent among ranking variables. Accepting the Kruskal-
Wallis H-test null hypothesis indicates agreement among the median ranking of variables
by experts in the four designated groups and between the group that identified model
variables and participated in the SMART questionnaire and those who only participated
in the SMART questionnaire. There was little agreement among the four identified
expert group in median ranks of the environmental model variables. The outlying expert
group in the median rankings for those environmental variables with p-values below the
significance level of 0.05 was identified as the business representative group.
Summarized result of the hypothesis testing used in this research can be found in
Table 4-12.
Table 4-12:
Hypothesis Statistics Result
1. There is correlation among all expert participants in Kendall RejectHo variable ranking.
2. There is a correlation within the Government Kendall Reject Ho Representatives expert group in variable rankings.
3. There is a correlation within the Impacted Business Kendall RejectHo"' expert group
4. There is a correlation within the Community Based Kendall Reject Ho expert group
5. There is correlation within the Science and Technical Kendall Reject Ho expert group
83
6. There is correlation within the expert group that defined model variables.
7. There is a correlation within the experts not involved in defining model variables
8. There is an agreement in ranking of model variables in the four expert groups
9. There is agreement in ranking model variables between those that defmed the model variables and those that did not
* ** *** ****
Rejected for four of five variables Rejected for three of five variables Accepted for twenty-five of thirty three variables Accepted for thirty-one of thirty three variables
Kendall RejectHo
Kendall RejectHo
Kruskal-Wallis AcceptH0
Kruskal-Wallis AcceptH0
The results of the statistical hypothesis testing indicate an agreement in ranking of
model variables for the resource allocation decision tool, indicating that the identified
model variables are valid for the model. The validity of model variables answers the third
research question: Can a decision model provide a useful tool and template to
decision makers for promoting resilience with consideration to future oil related
events? The decision model can provide a useful tool and template for policy makers.
4.5 Cumulative Percent and Prioritized List of Resource Allocation
The trade-off scores were placed in order of highest to lowest, with highest trade-
off score indicating the highest priority and the lowest trade-off score represents the
lowest priority. The ordering of trade-off scores provides a prioritized list of resilience
measures for addressing issues still relevant to the BP Deepwater Horizon incident as
well as future oil related incidents for St. Bernard Parish.
84
The cumulative percent model was implemented to optimize utility and address
scenarios of limited (and unknown) quantities offmancial resources associated with
penalties associated with the Deepwater Horizon. Established in past research (Mack,
2008, Jirapongsuwan, 2008, Zornes, 2007), the cumulative percent model has been used
to establish a minimum, expanded, and optimal list with cut-off points based on
percentage levels. The definition ofthe established lists can be found in Table 4-13.
Table 4-13:
List Cumulative Percent
Minimum 0-70%
Expanded 0-80%
Optimal 0-90%
The available funds resulting from BP penalties to be utilized for resilience measures in
St. Bernard Parish will be limited. Pending the outcome of litigation, policy makers can
utilize the appropriate list (minimum, expanded, or optimal) to implement according to
available funds.
4.5.1 Results for all expert groups
The model variables were sorted by trade-off scores from highest to lowest, thus creating
the prioritized list of resource allocation. This answers research question number four:
What are the weights of model variables and how are they prioritized? Trade-off
scores were then used in a cumulative percent list as a tool to assist policy makers in a cut
85
off mark for resource allocation when a minimum amount of funding is available. The
prioritized list of model variables and the resulting cumulative percent list can be found
in Table 4-14.
Table 4-14
Variables Minimum List Loans for Small Business Development Cypress Swamp Restoration Removal and Disposal of Residual Oil Restoration of Barrier Islands Aquaculture Studies River Diversions Outreach Programs to Promote Tourism/Fisheries Increase Mental Health Capacity Funding for Government Stipend for Business Adjustment to New Enviro Regulation Improve Capacity for Public Health Programs Construction of Oyster Reefs Improve Medical Services Expanded List Community Outreach and Support Programs Examine Sustainability of Fisheries Information Sharing Optimal List Public Health Staffing and Surveillance Database Monitoring Natural Attenuation of Residual Oil Basic Equipment for Residual Oil Clean Up Establish Monitoring System Bolster Education Programs Low Ranking Variables Streamline Recovery Fund Procedure Researching Impacts of Climate Change Education and Training Herbivory Prevention Parish Administrative Capacity Population and Demographic Studies Funds for Increased Community Meetings/Programs Language Translation
86
Trade-Off Score
0.0933 0.0808 0.0767 0.0598 0.0575 0.0501 0.0465 0.0404 0.0394 0.0376 0.0357 0.0343 0.0340
0.0336 0.0331 0.0304
0.0244 0.0239 0.0237 0.0215 0.0204
0.0203 0.0200 0.0187 0.0117 0.0111 0.0099 0.0065 0.0047
Cumulative Percent
9.33 17.41 25.08 31.06 36.81 41.82 46.47 50.51 54.45 58.21 61.78 65.21 68.61
71.97 75.28 78.32
80.76 83.15 85.52 87.67 89.71
91.74 93.74 95.61 96.78 97.89 98.88 99.53
100.00
The trade-off scores attained through expert input prioritize the model variables. The
list of variables has been separated into a minimum list, optimal list, and an expanded list.
The minimum list consists of all variables with a cumulative percent of less than or equal
to 70%. However this research for the minimum list is extended to 71.97% and will
include the community outreach and support programs as an exception. The expanded
list consists ofthose variables found with a cumulative percent within 80% of model
variables. The optimal list includes those rankings that account for 90% of the
cumulative percent list. This list contains twenty-two of twenty-nine variables, including
the streamline recovery funds procedure. Variables with a cumulative percent greater
than 90% do not meet the inclusion criteria for the optimal list and were not considered a
priority by the expert. The seven variables were researching the impacts of climate
change, education and training, herbivory prevention, parish administrative capacity,
population and demographic studies, funds for increased community meetings and
programs, and language translation. Monetary resources available for projects following
an incident like the deepwater horizon are significant but far from unlimited. Separating
the list into the minimum, optimal, and expanded list will be useful for policy makers to
address those variables with highest priority (weight) as indicated by study participants,
with limited financial resources.
Model variables under the economic stability and environment accounted for ten of
the fourteen variables within the 70% cumulative percent inclusion criteria for the
minimum list. Comments received by the researcher during the interview portion of the
SMART questionnaire indicated concern about the lack of employment diversification
and opportunities in St. Bernard and the need for innovative strategies to bring jobs
87
and/or create jobs in St. Bernard as a solution. Coastal erosions and hurricane protection
in St. Bernard was a second issue of concern raised in the interview sessions.
The societal impact objective had three variables found in the minimal list.
Increasing mental health capacity had the highest trade-off score of 0.0404, followed by
the need to increase public health capacity (0.0357) and improve medical services
(0.0340).
The variables in the logistical capacity for disaster response were not ranked high as a
group. No variables met inclusion criteria for the minimum list. The highest trade-off
score elicited from study participants was information sharing (0.0304), and this one
along with establish monitoring system (0.0215) and streamline recovery fund procedure
(0.0203) meet the inclusion criteria for the optimal list. The remaining variables were
above the 90% cumulative percent threshold for the optimal list.
4.5.2 Results for Expert Groups
In addition to the statistical analysis, the trade-off scores and cumulative percent
model were used to assess the four identified expert groups. Though these individual
lists are not considered in creating the prioritized list of resource allocation, they do
provide further analysis on the differences between expert groups. Their results are
found in Table 4-15 through Table 4-18.
The government representative expert group optimal list consisted of four of five sub
objectives found in the economic stability objective. They included loans for small
business development (0.1368), stipend for business adjustment to new environmental
regulation (0.0786), outreach programs to promote tourism and fisheries (0.0462), and
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funding for government services (0.0444). The researcher anticipated this expert group
would rank the economic stability variables highest. This hypothesis was anticipated due
to the continued recovery from the economic downturn in 2008. The number of
environmental sub-objectives included in the optimal list was low. Many ofthe
environmentally related sub-objectives were found towards the lower end of the list.
Funds for climate change, sustainability of the fisheries, monitoring of the natural
attenuation of oil, and herbivory prevention were all ranked in the lowest variables.
Three variables were selected, including restoration of barrier islands (0.1124), cypress
swamp restoration (0.0780), and river diversions (0.0534).
There were four sub-objectives from the societal impacts objectives. The highest
ranked ofthe four was the improvement of medical services (0.0509), followed by
increase in mental health capacity (0.0378), public health staffmg and surveillance
(0.0299), and improve capacity for public health programs (0.0222). The logistical
capacity for disaster response did not rank well among the science and technical expert
groups. Only one variable, streamline recovery fund procedure (0.0376), was included in
the expanded list with a cumulative percent of76.16% ofmodel variables. Parish
administrative capacity had a cumulative percent of89.12% of model variables, placing it
as the only variable within the objective category to be included in the optimal list, but
not the expanded list. The remaining five model variables had a cumulative percent of
90% or above. The government representatives who participated in this research did not
consider those variables important. The trade-off scores and cumulative percent for
model variables can be found in Table 4-15.
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Table 4-15:
Individual expert groups preference on resource allocation to promote the long-term resilience of St. Bernard Parish.
Government Representatives:
Trade-Off Cumulative Variable Loans for Small Business Development Restoration of Barrier Islands Community Outreach and Support Programs Stipend for Business Adjustment to New Enviro Regulation Cypress Swamp Restoration River Diversions Improve Medical Services Outreach Programs to Promote Tourism/Fisheries Funding for Government Increase Mental Health Capacity Streamline Recovery Fund Procedure Public Health Staffmg and Surveillance Database Improve Capacity for Public Health Programs Removal and Disposal of Residual Oil Population and Demographic Studies Bolster Education Programs Parish Administrative Capacity Aquaculture Studies Monitoring Natural Attenuation of Residual Oil Examine Sustainability of Fisheries Information Sharing Herbivory Prevention Establish Monitoring System Researching Impacts of Climate Change Education and Training Construction of Oyster Reefs Basic Equipment for Residual Oil Clean Up Language Translation Funds for Increased Community Meetings/Programs
90
Score Percent
0.1368 13.68 0.1124 24.92 0.0855 33.47 0.0786 41.33 0.0780 49.13 0.0534 54.47 0.0509 59.56 0.0462 64.18 0.0444 68.62 0.0378 72.40 0.0376 76.16 0.0299 79.15 0.0266 81.81 0.0222 84.03 0.0186 85.89 0.0166 87.55 0.0157 89.12 0.0157 90.69 0.0145 92.14 0.0117 93.31 0.0100 94.31 0.0093 95.24 0.0087 96.11 0.0083 96.94 0.0076 97.70 0.0071 98.41 0.0062 99.03 0.0058 99.61 0.0039 100.00
Business Representatives:
The business representative expert group had the highest level of agreement among
all expert groups for the economic stability sub-objectives. The level of cohesiveness can
be seen in the high level of prioritization of economic stability variables ranked by this
group. Four offive economic variables, loans for small business development (0.2437),
stipend for business adjustment to new environmental regulations (0.1044), outreach
programs to promote tourism and fisheries (0.1025), and aquaculture studies (0.053),
were not only included in the cumulative percent minimum list but ranked as the top four
prioritized variables by this expert group. These four variables account for a cumulative
percent for 50.36% of model variables. A large discrepancy was shown between the four
economic stability variables found in the minimum list and the remaining fifth economic
stability variable. Funding government services had a cumulative percent of92.38%
among model variables. The low level of prioritization of this variable excludes it from
any list considered in this research by the business expert group. There is a 2.5 fold
difference between the top ranked variable to the second highest ranked. The researcher
noted in the interview sessions with members of this expert group as strong preference in
the loans for small business development and innovation as critical to the survival of St.
Bernard Parish.
Following the high prioritization of economic stability variables, the environmental
and societal variables were ranked high. The environmental variables of cypress swamp
restoration (0.0364), sustainability of fisheries (0.033), and construction of oyster reefs
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(0.0296) were all within the 70% of the cumulative percent of model variables and
included in this expert groups expanded list. The variables of increasing mental health
capacity (0.0462) improve capacity for public health programs (0.0346) and improve
medical services (0.0321) fell within the inclusion criteria for the minimum list.
Similar to the government representative expert group, the business representative
expert group trade-off scores for the logistical capacity were identified to have low
priority among model variables. The information sharing variable (0.0270) was the
highest ranked variable from that objective and would be included in an expanded list for
this group. Two variables from this group would be included in the optimal list. The
remaining four variables fall below the inclusion criteria for the optimal list. To further
note the strong preference the business expert group had towards the economic stability
variables, the four highest ranked variables (all under the economic stability objective)
accounted for 50.36% of the cumulative percent for model variables while six variables
from the environmental and societal objective account for the next 20% that comprise the
minimum list for this expert group. The value-trade off score and cumulative percent for
the business representative expert group is found in Table 4-16.
Table 4-16:
Variable Loans for Small Business Development Stipend for Business Adjustment to New Enviro Regulation Outreach Programs to Promote Tourism/Fisheries Aquaculture Studies Increase Mental Health Capacity Cypress Swamp Restoration Improve Capacity for Public Health Programs Examine Sustainability ofFisheries
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Trade-Off Cumulative Score
0.2437 0.1044 0.1025 0.0530 0.0462 0.0364 0.0346 0.0330
Percent 24.37 34.81 45.06 50.36 54.98 58.62 62.08 65.38
Improve Medical Services Construction of Oyster Reefs Removal and Disposal of Residual Oil Community Outreach and Support Programs Information Sharing Basic Equipment for Residual Oil Clean Up Restoration of Barrier Islands Bolster Education Programs Education and Training Public Health Staffing and Surveillance Database Funding for Government Monitoring Natural Attenuation of Residual Oil Establish Monitoring System River Diversions Streamline Recovery Fund Procedure Population and Demographic Studies Language Translation Herbivory Prevention Researching Impacts of Climate Change
68.59 71.55 74.50 77.40 80.10 82.64 85.04 87.26 89.16 90.82 92.38 93.78 95.15 96.20 97.02 97.77 98.43 99.04 99.47 99.77 Funds for Increased Community Meetings/Programs
Parish Administrative Capacity
0.0321 0.0296 0.0295 0.0290 0.0270 0.0254 0.0240 0.0222 0.0190 0.0166 0.0156 0.0140 0.0137 0.0105 0.0082 0.0075 0.0066 0.0061 0.0043 0.0030 0.0023 100.00
Science and Technical:
The science and technical group ranking of model variables included more variables
from the four objectives than the government representatives or business representative
expert groups. Seven out of nine environmental variables were ranked in the expanded
list. The highest ranked environmental variables included the highest ranked variable of
removal and disposal of residual oil (0.2254), followed by monitoring the natural
attenuation of residual oil (0.0578), restoration of barrier islands (0.0517), funds to
examine the sustainability of the fisheries (0.0390), cypress swamp restoration (0.0379).
With the exception of the construction of oyster reefs variable (0.0173), the remainder of
the environmental variables are within 90% of the cumulative percent of model variables.
The highest ranked variable, removal and disposal of residual oil was ranked over three
times more important than the second ranked variable. The science and technical expert
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group did not prioritize the societal impact variables high, as two of eight variables would
be included in the expanded list. However, increased mental health capacity variable
(0.068) ranked as the second most important variable on the list. Community outreach
and support programs (0.0357) ranked within 70% of the cumulative percent for model
variables. Establish monitoring systems (0.0354) and information sharing (0.0336) were
the two variables from the logistical capacity for disaster response objective that meet the
inclusion criteria for the minimum list.
Despite aquaculture studies (0.0608) being the third highest ranked variable on the
list, the ranking of economic variables were considerably lower by this expert group in
comparison to other expert groups. Funding for government services (0.0444) was the
second highest ranked of the economic variables. These two variables were ranked
within the 70% cumulative percent for inclusion in the minimum list. Loans for small
business development (0.0282) was ranked substantially lower by this expert group than
the other three, though would still be included in the expanded list of priority variables
for resource allocation. Outreach efforts for promotion of fisheries and tourism (0.0240)
was ranked within the optimal list criteria. Funds for adjustment to new environmental
regulation (0.0114) rank was not sufficient to be included in the optimal list. Trade-off
scores and cumulative percent results for the science and technical group are found in
Table 4-17.
Table 4-17:
Variable Removal and Disposal of Residual Oil Increase Mental Health Capacity Aquaculture Studies
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Trade-Off Cumulative Score
0.2254 0.0680 0.0608
Percent 22.54 29.34 35.42
Monitoring Natural Attenuation of Residual Oil Restoration of Barrier Islands Funding for Government Examine Sustainability of Fisheries Cypress Swamp Restoration River Diversions Community Outreach and Support Programs Establish Monitoring System Information Sharing Researching Impacts of Climate Change Loans for Small Business Development Basic Equipment for Residual Oil Clean Up Outreach Programs to Promote Tourism/Fisheries Public Health Staffmg and Surveillance Database Improve Medical Services Bolster Education Programs Improve Capacity for Public Health Programs Construction of Oyster Reefs Stipend for Business Adjustment to New Enviro Regulation Population and Demographic Studies Education and Training Funds for Increased Community Meetings/Programs Streamline Recovery Fund Procedure Language Translation Parish Administrative Capacity Herbivory Prevention
Community Based Organizations:
0.0578 0.0517 0.0444 0.0390 0.0379 0.0360 0.0357 0.0354 0.0336 0.0334 0.0282 0.0242 0.0240 0.0234 0.0234 0.0229 0.0197 0.0173 0.0114 0.0098 0.0095 0.0075 0.0067 0.0051 0.0047 0.0031
The results of the trade off scores and cumulative percent showed less separation
between ranking model variables among the community based group. The community
based group has the largest number of variables that account for 80% of the cumulative
percent of the prioritized list, with seventeen variables. The list of also had the smallest
difference in trade-off scores between the variables that met the minimum list. Cypress
swamp restoration (0.1298), river diversions (0.0856), the removal and disposal of
residual oil (0.0592) were prioritized in the top five variables. Loans for small business
development (0.0606) and aquaculture studies (0.0474) were prioritized in the top five
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41.20 46.37 50.81 54.71 58.50 62.10 65.67 69.21 72.57 75.91 78.73 81.15 83.55 85.89 88.23 90.52 92.49 94.22 95.36 96.34 97.29 98.04 98.71 99.22 99.69
100.00
variables. Seven of nine environmental related variables met the criteria for an expanded
list. The community based expert group ranked herbivory prevention (0.0208) higher
than the other three expert groups and is within the 90% cumulative percent of model
variables. This ranking meets the criteria of for an optimal list.
The economic variables of outreach programs to promote tourism and fisheries
(0.33 8} and funding of government services (0.0241) met the criteria for an expanded list.
This expert group ranked four variables within the societal impact objective, bolster
education program (0.0 182), community outreach and support programs, population and
demographic studies (0.0075), and language translation (0.0029), in the lower third of
variables and below the inclusion criteria for an optimal list within this expert group.
Population and demographic studies and language translation were the two lowest
ranking variables in the prioritized list.
The variables within the logistical capacity for disaster response objective followed a
similar pattern in low prioritization as other expert groups. Education and training
(0.0337), basic equipment for residual oil removal (0.033), and streamline recovery fund
procedure (0.0197) ranked highest. The remaining variables in this objective group were
ranked below 90% of the cumulative percent for model variables. The trade-off scores
and cumulative percent of model variables for the community based expert group can be
found in Table 4-18.
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Table 4-18:
Variable Cypress Swamp Restoration River Diversions Loans for Small Business Development Removal and Disposal of.Residual Oil Construction of Oyster Reefs Aquaculture Studies Improve Capacity for Public Health Programs Information Sharing Restoration of Barrier Islands Outreach Programs to Promote Tourism/Fisheries Education and Training Researching Impacts of Climate Change Basic Equipment for Residual Oil Clean Up Improve Medical Services Examine Sustainability of Fisheries Public Health Staffing and Surveillance Database Funding for Government Establish Monitoring System Increase Mental Health Capacity Herbivory Prevention Streamline Recovery Fund Procedure Bolster Education Programs Parish Administrative Capacity Stipend for Business Adjustment to New Enviro Regulation Community Outreach and Support Programs Monitoring Natural Attenuation of Residual Oil Funds for Increased Community Meetings/Programs Population and Demographic Studies Language Translation
Comparison of Expert Results:
Trade-Off Scores
0.1298 0.0856 0.0606 0.0592 0.0561 0.0474 0.0450 0.0434 0.0417 0.0338 0.0337 0.0333 0.0330 0.0321 0.0309 0.0248 0.0241 0.0234 0.0217 0.0208 0.0197 0.0182 0.0171 0.0161 0.0150 0.0131 0.0100 0.0075 0.0029
There are distinct differences in trade-off scores between the expert groups in
Cumulative Percent
12.98 21.54 27.60 33.52 39.13 43.87 48.37 52.71 56.88 60.26 63.63 66.96 70.26 73.47 76.56 79.04 81.45 83.79 85.96 88.04 90.01 91.83 93.54 95.15 96.65 97.96 98.96 99.71 100.00
prioritization for resource allocation. Though the value-trade off scores and cumulative
percent of the four expert groups will not be used in the created resource allocation tool,
they provide additional analysis to the differences in values of the expert group. In
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summary, the economic stability sub-objectives were ranked high among all groups. To
provide more depth in assessing the prioritization of resilience measure per identified
objective, the following assessment utilizes the expanded list (80% of the cumulative
percent model). Four of the five variables were included within 80% cumulative percent
by three of four expert groups for each variable. Six ofthe environmental sub-objectives
were ranked within the 80% of the cumulative percent model by three of four expert
groups. Three variables from societal impacts followed the same ranking and just one
variable from the logistical capacity was ranked in the 80% of cumulative percent by
three of four expert groups.
4.6 Conclusion of Research Results:
The use of the Delphi method identified model variables for the resource allocation
tool. Fifteen experts participated in two rounds ofDelphi questionnaires of variable
selection and identified twenty-nine variables that met the inclusion criteria for model
variables. Thirty expert representatives of four identified expert groups (science and
technical, government representatives, business representatives, and community based
organizations) were approached to participate in ranking model variables. Participants
were asked to consider the use of funds from the BP deepwater horizon incident to
promote long-term resilience issues in their ranking of model variables.
The ranking procedure used in this research was the SMART model. Trade-off
scores were attained by averaging the study participant's response for each variable.
There were four objective variables and twenty-nine sub-objective variables. The
averaged response to each objective was multiplied by the averaged response to each
corresponding sub-objective within that objective. The attained trade-off scores for each
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variable were sorted from the highest score to the lowest, thus creating the prioritized list.
The cumulative percent model was implemented, and cut-off points, established from
previous research, were implemented to identify a minimum, expanded, and optimal list
of resilience measures to be addressed in St. Bernard Parish with consideration of future
oil related incidents. The variables found in the minimum list (highest priority for
research objectives) are addressed below in table 4-19.
Table 4-19:
Minimum Variable List
Variables Trade-Off Score Cumulative Percent
Loans for Small Business Development Cypress Swamp Restoration Removal and Disposal of Residual Oil Restoration of Barrier Islands Aquaculture Studies River Diversions Outreach Programs to Promote Tourism/Fisheries Increase Mental Health Capacity Funding for Government Stipend for Business Adjustment to New Enviro Regulation Improve Capacity for Public Health Programs Construction of Oyster Reefs Improve Medical Services
0.0933 0.0808 0.0767 0.0598 0.0575 0.0501 0.0465 0.0404 0.0394
0.0376 0.0357 0.0343 0.0340
Ten of thirteen sub-objectives considered as highest priority for implementation to
build resilience against future oil related events impacting St. Bernard Parish were found
in the economic and environmental categories. The emphasis of the environmental sub-
objectives found in the minimum list were not sub-objectives directly impacted by the
Deepwater Horizon, rather sub-objectives more closely related to coastal erosion and
general ecosystem health in St. Bernard Parish. The high prioritization of these
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9.33 17.41 25.08 31.06 36.81 41.82 46.47 50.51 54.45
58.21 61.78 65.21 68.61
environmental sub-objectives indicates a broad concern for the high rate of coastal
erosion and hurricane protection. Important to mention, the highest of prioritized
environmental variables associated with coastal erosion and hurricane protection do
create indirect resilience to future oil related incidents by stabilizing the ecosystem.
Kendal's Coefficient of Concordance (W) was used to assess the level of correlation
among all groups and within the expert groups. Levels of correlation were found to be
significant, but low among all study participants. The highest level of agreement was
found in ranking the societal impact variables with W = 0.342 and the lowest level of
agreement found in ranking the economic sub-objectives W = 0.142. A higher level of
agreement was found within the expert groups. The highest agreement came within the
business representative expert group. Levels of agreement ranged from W = 0.617 for
the economic stability variables to 0.354 for societal impact. The science and technical
expert group had the highest level of agreement (W = 0.434) for environmental variables
and the lowest level of agreement on economic stability variables at W = .282.
Community based groups had a level of agreement ranging from W = 0.494 (objective
ranking) toW= 0.235 for economic variables.
Overall the level of correlation on ranking of model variables was low. These results
can be interpreted in several fashions. The development of the survey and collection of
data for this research was completed as the full impacts of the oil related incident was
being assessed. Without complete and sound data on the extent ofthe impacts of the
Deepwater Horizon event, the experts are more likely to rank variables they feel most
relevant to the long-term resilience for St. Bernard. This may include measures that were
directly or indirectly impacted from the Deepwater Horizon event.
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The null hypothesis of Kendall's Coefficient of Concordance was rejected for the
seven hypotheses in this research, indicating significance in the levels of correlation
(Tables 4-3 to 4-9). The Kruskal-Wallis H-test null hypothesis, indicating no difference
in median ranking among the previously mentioned study participant groups, was
accepted for the two hypotheses tested in this research (Tables 4-10 and 4-11 ).
Trade-off scores and cumulative percent for model variables were calculated for the
four expert groups. These results were not used in the creation of the final resource
allocation tool, rather to provide additional analysis and insight to the values and
preference of model variables within expert groups.
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5.1 Overview of Results:
CHAPTERV
DISCUSSION
The views and values obtained through the SMART questionnaire are based on the
individual expert's personal experiences in dealing with the short comings of distribution
of funds and the needs of St. Bernard Parish following the BP Deepwater Horizon oil
leak of2010. Kendall's Coefficient of Concordance r,:w) was used to assess the
correlation in responses between the four expert groups as well as within each expert
group. The Kruskal-Wallis H-test was used to test the median ranking ofmodel variables
to assess if significant agreement existed between the experts. Additional statistical
analysis tests were conducted to assess correlation and agreement between the expert
group who identified the model variables during the Delphi method and participated in
the SMART questionnaire with those experts in expert groups who only participated in
the SMART questionnaire. The following analysis answers research question number
five: What can be learned through the development of this model?
The issues contributing to the lack of resilience in St. Bernard Parish are complex and
significant. Major issues facing the Parish are slow recovery due to weakened resilience
from past disasters, the net loss of sediment leading to coastal erosion (as a result of
extensive levee systems in Louisiana), subsidence, salt water intrusion, decreased storm
protection as a result of an unhealthy deltaic system, a comparatively economically
depressed region in the United States, and lack of adequate public health infrastructure.
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Though this study asked expert participants to identify measures that will build
resilience against future oil related incidents in the Gulf of Mexico through utilizing the
Delphi method, many identified variables were not directly associated to oil related
incidents. The variables not pertaining to future oil related incidents, however, will
contribute towards creating an increased general resilience by improving the diversity of
economic opportunities, creating and rehabilitating a fragile ecosystem, and improving
quality of life for the residents of St. Bernard Parish. This is likely a result that occurred
due to the expert participants concerns about the long-term viability of the Parish and a
realization that the threat of future oil related incidents in the Gulf is one of many
resilience issues needing to be addressed in the Parish. In an area that is vulnerable to
both man-made and natural disasters, these measures will provide means for creating
resilience, and mitigating the initial and long-term impacts associated with future
disasters. Those identified variables that establish general resilience measures in St.
Bernard Parish addressed issues such as coastal erosion and strengthening natural and
man-made resources against future disasters.
Surprisingly, a major issue that impacts the sustainability and economic viability the
Louisiana Gulf Coast was not addressed. The hypoxic zone found off the Louisiana coast
is currently the size ofNew Jersey and Delaware. This hypoxic zone (a zone that has less
than 2mg/L of dissolved oxygen), is a result of excess nutrient flow associated with
Midwestern agriculture run-off in the Mississippi River, and creates an environment that
is inhospitable to nearly all aquatic species. In terms of building long-term resilience to
eco-system services in the Gulf of Mexico, measures addressing and mitigating the
hypoxic zone (dead-zone) must be established and implemented.
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As the Mississippi Delta continues to degrade, St. Bernard becomes increasingly
dependent on oil and gas operations and fisheries as means for economic stability. Both
sectors have a high degree of dependence on the environment for their operations and are
susceptible to major slow down should a disturbance cause shock to the environment.
This dependence on such few opportunities for economic livelihood and the dependence
of which those opportunities have on the declining health of the ecosystem is a core issue
highlighting the lack of resilience in St. Bernard Parish. The recognition of the
interconnectedness between the economic viability of St. Bernard and a healthy
ecosystem was indicated by expert responses through their value trade-offs. The highest
rated variables in the created prioritized list were those associated with the environmental
and economic stability object groupings.
The elicited responses from expert representatives of expert groups ranked the
environmental objective most important, accounting for 39.04% of total importance
followed by the economic stability objective (27.44%). The societal impacts objective
was ranked third in importance at 20.31%, and the logistical capacity for disaster
response was ranked as the least important of the four objectives (13.22%).
Interestingly, in addition to selecting resilience measures that promote general
resilience by the fifteen experts who identified model variables (Delphi method), those
measures were highly prioritized by the additional twenty-one experts who participated in
the SMART portion ofthe study. This further illustrates the view of the expert
participants in the importance to approach resilience in St. Bernard in a generalized
manner. Resilience is a concept that spans many facets of society and building resilience
in a more general sense will provide an ability for the community to recover quicker from
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any disaster, in contrast to a specific disaster, such as future oil related incidents in the
Gulf. Measures such as cypress swamp restoration, construction of berms, and loans for
small business development will create a more generalized approach towards building
resilience to future man-made or natural disasters in St. Bernard Parish.
Resilience measures associated with logistical capacity for disaster response
following an oil related incident had the lowest prioritization among the four sub
objective groups. This further underscores the sentiment among expert participants to
allocate available funds towards general resilience measures that will prepare St. Bernard
Parish for future man-made or natural disasters opposed to specifically addressing
resilience towards future oil related incidents.
Model variables that were ranked highly were primarily dealing with the continued
fallout and on-going concerns with the impacts associated with the BP Deepwater
Horizon incident. This is illustrated in the high rankings of removal and disposal of
residual oil, aquaculture studies, outreach programs to promote tourism/fisheries, and
stipends for business adjustment to new environmental regulations. This view by expert
participants indicates that priorities addressing impacts associated with the Deepwater
Horizon event and focusing on generalized resilience in the Parish (as opposed to
specifically preparing for a future oil related incident) as the most beneficial approach
towards addressing the long-term viability of St. Bernard Parish.
An alternative explanation to the identification and high prioritization of resilience
·measures not directly associated to future oil related incidents in the Gulf of Mexico is
the timing in which the questionnaires and data were collected. The data collection phase
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of this research was conducted soon following the BP Deepwater Horizon incident. The
size and nature of this type of incident had never occurred in the Gulf of Mexico and the
long-term impacts associated with the incident are still unknown. The timing of this
project may have resulted in the selection of variables not directly associated with the BP
Deepwater Horizon incident and provides an explanation of why some identified
variables did not have a direct relation to building resilience in St. Bernard Parish.
5.2 Hypothesis Testing:
The hypothesis testing revealed interesting insight on the values and importance
placed on model variables. This section will provide an analysis of Kendall's Coefficient
of Concordance (W) for all experts and the expert groups. Low levels of agreement on
the prioritization of resource allocation for model variables was found among study
participants. A potential explanation for this result is the broad and diverse nature of this
study and study participants that represent a wide range of professions. The participants
had varying, and at times conflicting, views on what was of importance to the long-term
sustainability of St. Bernard Parish. The conflicting rankings by participants indicate a
possible lack of understanding and or recognition of the integrated role the coastal
ecosystem plays in the economic stability of St. Bernard Parish. The low level of
agreement among all study participants indicate a disconnect between expert groups in
identifying and establishing a cohesive approach towards long-term resilience in St.
Bernard Parish. This is of particular concern due to the critical and immediate resilience
measures needed to maintain viability in St. Bernard. Cohesive political will is required
to promptly address and implement resilience measures
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The highest level of agreement was found in the ranking of societal impact sub
objectives at W=0.325. This result was not anticipated; however it indicates a cohesive
view among the expert groups in approaching the societal based needs of St. Bernard
Parish. Although the societal impacts sub-objectives were the most agreed on by study
participants, the societal impact objective was prioritized behind the environmental and
economic stability objectives. The economic sub-objectives had the lowest level of
agreement at W= 0.142, yet was ranked high in the resource allocation list, of which one
was the highest ranked variable: loans for small business development.
Analysis of agreement within each of the four expert groups indicated a higher level
of agreement within each group when compared to levels of agreement when all thirty
participants were placed in one group. This result indicates study participants within an
expert group with similar backgrounds and training tend to have the same perception in
prioritization of variables and the needs of St. Bernard Parish.
5.3 Review of Objective Ranking:
Significance in association of ranking of model objectives was not found for the
government expert group, the impacted business expert group, and the science and
technical expert group. Significance levels were established at a p-value of0.05.
There are several reasons as to why there was lack of significance. The first is that at
the time of data collection, the full impacts of the BP Deepwater Horizon were unknown.
This likely led experts to select resilience measures they felt important towards building
resilience for storm protection and coastal erosion. These measures build general
resilience in St. Bernard Parish against future man-made or natural disaster scenarios, and
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therefore indirectly build resilience for future oil related incidents. The second reason is
likelihood that experts' personal political viewpoints contributed to lack of significance
associated with correlation among experts in ranking objectives in this research. It is
observed that environmental issues have become highly politicized in this country.
The low levels of correlation (Kendall's Coefficient of Concordance) among ranking
the environmental variables as well the lack of agreement found in the median rank of
environmental variables (Kruskal-Wallis H-test), indicate the complexity and controversy
behind implementation of the environmental restoration variables. As environmental
related issues have become politicized in the United States, there is a likelihood that
experts political viewpoints contributed to the lower levels of correlation in Kendall's
Coefficient of Concordance in objective ranking and disagreement in the Kruskal-Wallis
H-test agreement in median rank of model variables. This is extrapolated as the level of
agreement found within the business representative group had high levels of agreement in
pro-economic growth variables, yet differed in priority from the remaining three expert
groups. In the current political climate, pro-economic growth ideology tends to be closer
associated with conservative party recommendations. This study did not confirm the
political affiliation of each expert participant.
An issue impacting p-values are the sample sizes of the individual groups. The
groups that lacked significance had a smaller sample sizes (n = six for the Government
Representative expert group, six for the Business Representative expert group, and seven
for the Science and Technical expert group). It is likely that with larger sample sizes, the
p-values would be lower, thus leading to significance in the statistical analysis.
108
5.4 Review of Environment Objective and Sub-objectives and Results:
The environment was ranked as the most important objective in the decision model
with a ranking of trade-off score of 0.3904 for all thirty study participants. Six of nine
sub-objectives related to this objective are part ofthe optimal list of the cumulative
percent of75%. The highest ranked variable was cypress swamp restoration, followed by
removal and disposal of any residual oil that is found, then restoration of barrier islands,
followed by river diversions, construction of oyster reefs, and examination of the
sustainability of the fisheries. At the outset of this study, the researcher's hypothesis was
that the environmental objective would be considered by study participants to be the most
important one to address and its corresponding sub-objectives would be highly ranked.
The interview sessions and questionnaire responses indicated a strong understanding and
sentiment to the importance of environmental protection from both man-made and natural
disasters and restoration in St. Bernard Parish to maintain its long-term viability.
Significance in association in ranking environmental variables was established by all
groups. The levels of association identify a fundamental difference in which resilience
measure should be implemented first. The nature and economic utility of coastal
Louisiana ecosystem is complex. Experts with an economic stake in the development
and health of certain aspects of the ecosystem, which benefit their livelihood, are more
likely to place a higher prioritization on variables that promote their specific
field/industry. For example, those with a stake in the oyster industry had oyster reef
construction and river diversions highly prioritized.
109
5.5 Review of Logistical Capacity Objective and Sub-Objectives and Results:
The variables in this objective were selected to gauge the allocation of funds towards
establishing a preparedness plan and having all the necessary resources available for the
smooth response to a large-scale oil related event. The logistical capacity for disaster
response was the lowest rated objective accounting for 13.22% of the overall importance
of model objectives. The sub-objectives as a group were subsequently ranked low as
well. The highest ranked variable, and the only variable that fell within the top 80%
limit of the cumulative percent model to be included in the expanded list was the sub-
objective for information sharing (3.04%) followed by basic equipment for residual oil
clean up, establishing monitoring systems, education and training, parish administrative
capacity, and funds for increased community meetings and programs. The lowest ranked
variable in this objective was ranked second lowest on the complete list of resource
allocation. These results indicate a confidence of study participants in the development
of a preparedness plan on a state and local level and the handling of the execution phase
of the plan.
5.6 Review of Economic Objective and Sub-Objectives and Results:
The economic stability objective group was prioritized second behind the
environmental objective accounting for 27.44% ofthe overall importance. This objective
had all five sub-objectives ranked within the top 50% of the cumulative percent model,
including the top overall variable of loans for small business development. On the outset
of the study the researcher anticipated the sub-objectives within the economic stability
objective to be ranked high, as mentioned in chapter four. The results capture the general
110
sentiment of the region's priority on economic development and job growth. It was
mentioned by several study participants the importance of innovation throughout coastal
Louisiana as a way to maintain the long-term viability ofthe region. Further, the
comments suggested that as the opportunities of employment are primarily limited to oil
and gas, tourism, and fisheries along the gulf coast, a strong effort was needed to
encourage citizens to create jobs through innovation. The participants who commented
on this did not provide further details on which sector they would like to see have an
increased presence in the region. The overall level of correlation by all thirty participants
was very low (0.142). This can potentially be attributed to the diverse focus of expert's
professions and perceptions on economic measures that will optimize growth. The
Business representative expert group had the highest level of correlation in this set of
sub-objective. This is likely a result that most participants in this expert group were
business executives whose experience in focusing on predicting needs and changes in the
local business atmosphere in order to maintain a profitable firm.
5. 7 Review of Societal Impacts Objective and Sub-Objectives and Results:
The societal impacts resulting from an oil related event similar to that of the
Deepwater Horizon oil leak on St. Bernard Parish is significant. This objective was
ranked with the third highest importance behind the environmental objective and
economic stability objective at 0.2031. Four of the seven variables ranked within the
minimum list, with the highest ranked variable being a focus on mental health. Residents
of St. Bernard are still recovering from the psychological trauma associated with
Hurricane Katrina (GAO, 2009, Rhodes, et. al. 2010). Though results from mental health
studies following the Deepwater Horizon incident have not been reported, mental health
111
studies following the Exxon Valdez spill in Prince William Sound have indicated a
likelihood of mental health impacts from the BP incident (Gill, 2011 ). Over twenty years
later, mental health issues are still pervasive as a result of the Exxon Valdez oil spill. St.
Bernard Parish set a priority to upgrade mental health and medical facilities in the Parish
to adequately handle the need following Hurricane Katrina as well as for any future
events (Mitchell, 2007). Unfortunately, mental health facilities are limited and there is no
hospital in St. Bernard. The remaining variables were to improve capacity for public
health programs, improve medical services, and community outreach and support
programs.
5.8 Policy Implications:
The BP Deepwater Horizon incident was the first major oil related incident occurring
in a body of water in the United States since the Exxon Valdez oil spill in 1989 which
resulted in the release of over 250,000 barrels. The recent BP incident highlighted flaws
in the oil pollution act of 1990 (OPA), which was legislation created as a result of the
Exxon Valdez; it is designed to address oil tanker spills and not off-shore drilling events.
Thirty percent of the United States national oil production comes from drilling operations
in the Gulf of Mexico (DOE, 201 0) , and there is a likelihood of a similar incident
occurring in the future. Policies need to be updated to address and to include current
national status of oil and gas production. The response to oil spill legislation should
approach different ecosystems, spill or leak sizes, and take an integrated approach to
restoration in affected areas.
112
The OPA fmes are levied by taxing the responsible party 18.75% per barrel at the
average barrel market cost. OP A fmes resulting from the Deepwater Horizon oil leak
would cost BP approximately 78.1 million US dollars (USD). This number is based on
4.9 million barrels of oil leaked, with a market average of 85 USD per barrel taxed at
18.75%. The price per barrel is likely to change based on the fluctuating market price of
oil.
In addition to the OPA fmes, the Clean Water Act fines would be assessed as well.
Current legislation dictates the CW A fme to be levied on the responsible party per barrel
of oil leaked. The fine per barrel ranges from $1,100 to $4,300 (Federal CWA). The
specific fine relates to the court's ruling on levels of gross negligence. The resulting
fmes from court ruling are currently distributed to the National Oil Spill Trust Fund and
the United States Treasury.
Following the prospectus defense for this research, The Resource and Ecosystem
Sustainability, Tourist Opportunities, and Revived Economy of the Gulf Coast Act of
2011 (RESTORE the Gulf Act of2011) was introduced by a bi-partisan group of
Senators from the Gulf States (Mary Landreau, Thad Cochran, Jeff Sessions, and Kay
Hutchinson). If passed, the legislation would require that 4/5 (80%) of funds from the
Clean Water Act funds be distributed to the five Gulf States that were affected by the BP
Deepwater Horizon spill. This legislation would create a subsection in the Federal Water
Pollution Control Act to allow for the allocation of CW A funds to the Gulf Coast. The
subsection indicates that 35% of the funds are to be equally distributed to the five Gulf
States .. Sixty percent (60%) ofthe funds would be allocated the Gulf Coast Ecosystem
Restoration Council, and 5% of funds would be distributed for research and science
113
--- ----------------------------
programs in the Gulf coast region. Of the 35% that is to be equally distributed to each
state, it is up to the Governor of each state as well as state agencies to decide on the
appropriate distribution ofthe funds (Environmental Defense fund, 2011). Currently
there is no method described by the National Oil Spill Commission or the Gulf Coast
Ecosystem Restoration Task Force to prioritize tasks that need to be addressed with the
recommended distribution of funds (Final report, 2011 ).
The resource allocation tool developed in this research approaches this complex
problem through a recognized method in the decision sciences. The created tool is only
relevant to the needs of St. Bernard Parish, LA., where the research project took place.
Expert groups in different Gulf of Mexico coastal areas may respond and provide
different results for their particular geographical area.
The decision analytic tool developed by this research should be considered by policy
makers in Baton Rouge, LA and state government agencies as a recommendation to the
distribution of funds. As dictated by the RESTORE the Gulf act, The Gulf Coast
Ecosystem Restoration Council will receive 60% of the CWA funds. A principle
objective for the council is to develop a comprehensive plan addressing restoration and
protect ecosystems, natural resources, wetland.s, and natural resources (RESTORE the.
Gulf, 2011). In October, 2011, the Gulf Coast Ecosystem Restoration Task Force
released its preliminary report, Gulf of Mexico Regional Ecosystem Restoration Strategy
on the issues facing the Gulf Coast. The report is anticipated to be used for guidance to
the Council should the RESTORE act be made into law. The resource allocation tool can
be utilized by the council to address restoration issues as well as the preparedness of St.
Bernard Parish in the event of a future oil related event in the Gulf of Mexico.
114
The second use of this research in the RESTORE the Gulf Act of2011 is the
distribution of 35% of CW A funds to the five Gulf States impacted by the Deepwater
Horizon event. The allocations of funds will be distributed equally to the five Gulf
States. The use of this fmancial resource is to be allocated at the discretion of the state
government (the Governor's office and state agencies). The likely distribution of funds
for Louisiana will be made in close consultation with the Office of Coastal Protection and
Restoration and referencing the State Master Plan. The Louisiana State Master Plan was
developed to address the critical needs of coastal Louisiana in 2007. It is updated every
five years to address the quickly changing needs and issues facing coastal Louisiana. The
resource allocation tool can be considered as a method for prioritizing the allocation of
limited monetary resources in the 2012 State Master Plan. In the event that the
RESTORE the Gulf Act of2011 (or future legislation addressing the Clean Water Act
fines to be allocated towards resilience efforts in the Gulf) does not pass into law, the
resource allocation tool remains relevant in consideration for future updates of the
Louisiana State Master Plan.
The State Master plan of Louisiana is scheduled to be released for public comment in
March of2012. The Master Plan is designed to approach the coastal needs of Louisiana
and is updated every five years. Though this plan has indirect economic benefits to
Louisiana citizens, The State Master Plan focuses on environmental projects to reduce
coastal erosion, promote the wetlands, and an overall healthy ecosystem. The anticipated
funding for the 2012 State Master Plan comes from $80 million per year from Coastal
Wetlands Planning, Protection and Restoration Act (CWPPRA), $110 million per year
from the Gulf of Mexico Energy Security Act (GOMESA), and $150 million per year
115
from Louisiana Coastal Area (LCA) Program (Draft State Master Plan F AQ, 2012). The
finances associated with implementation of projects in the State Master Plan are
anticipated, but not guaranteed.
5.9 Public Health Pertinence:
Oil and gas exploration and production provide an important source of economic
stability in Louisiana for many years. The role of oil and gas operations will play an
increased importance to Louisiana as sustained political pressure in the United States
works towards energy independence. With oil related operations above 3,800 in 2011 in
the Gulf of Mexico and anticipated to increase, the probability of a future oil related
incident is high.
A cornerstone of the public health profession is the emphasis on preparedness and
prevention. Allocating funds from the BP oil leak towards long-term resilience issues in
St. Bernard Parish will strengthen the environment and coastal communities'
preparedness to mitigate damages associated with such a future event. For example, had
. the identified model variables in this research been implemented prior to the Deepwater
Horizon incident, impacts of the moratorium on oil related operations and fisheries would
have been reduced due to the diversification of employment opportunities, less oil would
have reached the coast due to the community being better suited to handle an oil related,
and the environmental impacts on the coastline would have been mitigated. Addressing
the lack of resilience and preparedness in coastal Louisiana and other. gulf states is a
concept addressed in the National Commission on the BP Deepwater Horizon Oil Spill
and Offshore Drilling final report and the preliminary report by the Gulf Coast
116
Ecosystem Restoration Task Force. A study on the validity and cost-effectiveness of
allocating resources to preventative measure in community based projects was addressed
by the Multi Mitigation Council and concluded that for every one dollar spent towards
hazard mitigation equates to a four dollar saving (MMC, 2005). In order to optimize
resilience and preparedness in coastal communities, an effective strategy to prioritize the
distribution of monetary funds must be implemented
The decision analytic tool created in this research provides a prioritized list of
resilience measures that promote resilience to future oil related incidents as well as
establishes generalized resilience in St. Bernard Parish. Monetary resources are limited in
these situations and it is critical to efficiently utilize the limited funds. Prior to
implementation of any measures associated with this research, a cost assessment must be
conducted for each variable. When the cost of implementation is accounted for, the
policy maker must decide on which measures are feasible to implement with the available
funds. For example, if there are three resilience measures with lower priority that can be
implemented at a cost equal to that of a higher priority, the decision maker must decide
on which choice will create greater resilience. Further, an assessment of projects found
on this prioritized list with secured funding must be identified to minimize duplication of
projects.
The benefits of implementing the prioritized list of resilience measures created in this
research in the short term are large. Job creation through the small business loans as well
as the implementation of the construction and labor employment resulting from the
implementation of many variables will provide an economic boost to the region. The
correlation between the economic stability of a community and the health of the
117
community is strong (Bloom, 2008). Upon the completion of implementing the selected
measures, the increased resilience will mitigate the impacts of any future disasters in the
Parish. This will reduce the physical and psychological, as well as ecological and
economic impacts a future disaster has in St. Bernard Parish.
Many of the variables in the optimal list promote coastal restoration. The restoration
projects will rehabilitate a depleted ecosystem, leading to greater bio-diver~ity and a
reduction of environmental factors causing degradation such as salt-water intrusion and
coastal erosion. The resulting measures will benefit St. Bernard ecosystem services by
creating a healthier ecosystem and better subsequent productivity in the commercial and
tourist fisheries sector. Further, these measures will build resilience to the impacts of
future anthropogenic or natural disasters. The implementation of the societal needs will
provide basic services, mostly pertaining to health and education that are needed in St.
Bernard.
5.10 Conclusion and Summary:
Coastal Louisiana's vulnerabilities to natural and man-made disasters continue to be
exposed. Disasters, such as the BP Deepwater Horizon incident, further degrade the
environment; weaken the strength and resources of communities, and impact individuals'
health and well-being. With the recent passing of the RESTORE the GULF Act of2011,
significant quantities of financial resources (5.3 to 21 billion dollars pending on level of
negligence identified by the Federal Court in New Orleans, LA) resulting from civil
penalties associated the Clean Water Act will be allocated towards building resilience in
Gulf of Mexico states.
118
The results of the research conducted in this study should be considered by policy
makers at the state and federal level as an effective method to identify and prioritize
resilience measures for St. Bernard Parish. However, it is essential that prior to the
implementation of resilience measures to conduct a cost assessment of each measure.
The recommended and highest prioritized resilience measures in this research will likely
come with significant costs. Upon completion of the cost assessment, the policy maker
must consider which resilience measure is feasible to implement and/or if it would be a
better utilization of monetary resources to fund multiple projects that do not have the
highest of prioritization but deemed more cost effective~ Further, prior to implementation
the policy maker must assess if funding has been secured to implement any resilience
measures that have been identified with in the prioritized list found in this research.
The research created the prioritized list by sorting resilience measures with the
highest trade-off score (representing highest priority) to the lowest trade-off score
(representing the lowest priority). Based on previous research that utilize the SMART
methodology (Mack, 2008, Jirapongsuwan, 2008, Zornes, 2007), the cumulative percent
model was implemented to establish three lists: Minimal list, Expanded list, and Optimal
list. The aforementioned list provides the policy maker with a reference point in which to
approach implementing tasks based on the availability of funds. Table 5-l illustrates the
breakpoint for each list.
119
Table 5-1:
List Cumulative Percent
Minimum 0-70%
Expanded 0-80%
Optimal 0-90%
The available funds resulting from BP penalties to be utilized for resilience measures
in St. Bernard Parish will not be adequate to address all identified resilience measures.
Pending the outcome of litigation, policy makers are recommended to utilize the
appropriate list (minimum, expanded, or optimal) to implement according to available
funds. The breakdown of the lists are as follows:
Minimum Variable List
Variables Trade-Off Score Cumulative Percent
Loans for Small Business Development Cypress Swamp Restoration Removal and Disposal of Residual Oil Restoration of Barrier Islands Aquaculture Studies River Diversions Outreach Programs to Promote Tourism/Fisheries Increase Mental Health Capacity Funding for Government Stipend for Business Adjustment to New Enviro Regulation Improve Capacity for Public Health Programs Construction of Oyster Reefs Improve Medical Services
120
0.0933 0.0808 0.0767 0.0598 0.0575 0.0501 0.0465 0.0404 0.0394 0.0376 0.0357 0.0343 0.0340
9.33 17.41 25.08 31.06 36.81 41.82 46.47 50.51 54.45 58.21 61.78 65.21 68.61
Expanded Variable List
Variables Trade-Off Score Cumulative Percent
Community Outreach and Support Programs Examine Sustainability of Fisheries Information Sharing
Optimal Variable List
0.0336 0.0331 0.0304
71.97 75.28 78.32
Variables Trade-Off Score Cumulative Percent
Public Health Staffmg and Surveillance Database Monitoring Natural Attenuation of Residual Oil Basic Equipment for Residual Oil Clean Up Establish Monitoring System Bolster Education Programs
Low Ranking Variable List
0.0244 0.0239 0.0237 0.0215 0.0204
80.76 83.15 85.52 87.67 89.71
Variables Trade-Off Score Cumulative Percent
Streamline Recovery Fund Procedure Researching Impacts of Climate Change Education and Training Herbivory Prevention Parish Administrative Capacity Population and Demographic Studies Funds for Increased Community Meetings/ Programs Language Translation
0.0203 0.0200 0.0187 0.0117 0.0111 0.0099 0.0065 0.0047
The variables in the Minimal list are those considered the highest priority to
implement, followed by those in the Expanded list, and then those in the Optimal list.
The Low Ranking Variable List, variables found from 90%-1 00% in the cumulative
percent model, needs to be considered for implementation. The likely cost associated
with each measures is low and variables (such as education and training, parish
121
91.74 93.74 95.61 96.78 97.89 98.88 99.53
100.00
administrative capacity, and streamline recovery fund procedures) will effectively build
resilience in St. Bernard Parish. Additionally, the ranking of funds to research climate
change in the Low Ranking Variable List is of concern. Coastal Louisiana will be
drastically impacted with sea level rise, which results from climate change. The full
impacts need to be assessetl and mitigating solutions must be considered to address this
ISSUe.
Not all resilience measures identified and prioritized in this research directly
approach building resilience for future oil related incidents in St. Bernard Parish. The
measures, however, are important in building long-term resilience against future man
made and/or natural disasters for the Parish. With extensive environmental degradation,
limited employment opportunities, and lack of basic societal infrastructure, it is
imperative to address these issues with urgency to ensure the long-term viability of St.
Bernard Parish. As government budgets continue to be cut and funding sparse, the
secured funds associated with Clean Water Act civil penalties stemming from the BP
Deepwater Horizon incident will be dedicated towards implementing resilience measures
in the Gulf Coast. The results of this research is recommended as a guideline to identify
and prioritize needed resilience measures in St. Bernard Parish.
122
CHAPTER VI
LIMITATIONS AND RECOMMENDATIONS
6.1 Limitations:
The following are identified limitations of the study:
•
•
The results of this survey reflects the opinions of those surveyed .
The study defines long-term as 50 years; at the current rate of coastal erosion there is
concern about the quantity of landmass in St. Bernard Parish. The study does not
address allocating resources to relocation of populations or studies assessing the long
term viability of St. Bernard communities.
• This research was based on a recent disaster and captures the values and viewpoints
of expert representatives of the mentioned expert groups at a given time. The views
and values of study participants may change considerably as research and information
becomes available.
• This study does not include the cost of implementing each model variable.
• This study is limited to the needs of St. Bernard Parish, LA
123
6.2 Recommendations
The following are recommendations of the study:
• The trade-off scores were based off of study participant's views on prioritization of
model variables primarily based in their involvement and experiences of the recovery
process following the BP deepwater horizon oil leak. As the full extent of long term
impacts are still under research, it is recommended this study be updated in five years
time to assess the views of expert groups.
• As part of an update to this study, it is recommended that each objective be assessed
in more depth. A decision model should be developed on each objective in this study,
with a prioritization given to the environmental, economic stability, and societal
impact objectives.
• Perform an assessment on the cost of implementing each model variable and perform
a future assessment on the prioritization of the variables with this consideration. At
the conclusion of the assessment, a question should be structured to be included on if
the cost -effectiveness of a measure influenced its prioritization.
• Expand the sample size of study participants to fifty. This would provide additional
results to be compared with this study.
• Implement an optional question to the study participants regarding their political
affiliations in five years, at the time of the updating of the research.
• Complete similar study on resource allocation in emergency response phase of a
large-scale oil related incident in the Gulf of Mexico
124
Appendix A: IRB Approval
Tulane Universitv Human Research Protection Program..!
Ttl fan;.:. Enman R::..-sq:rrc/1 ?rorecrh:r£ .. ,~TC::J:''l1m Jn;;inuicnc:I Ro.-:o.·t.::-.r 3<Jards Biomedical Sc.:t[:J Bdu:r:iorr:I .::;-o:~aaoo:.:o.~5
DATE: September 2'1, 201'1
TO: Benjamin Schulte, r-.lP.H. FROt-.-1: Tulane University Sociai-BehaviomiiRB
STUDY TITLE: [2:35200-'1] Deepwater Horizon Oil Leak: A Decision Analytic Approach to Resource Allocation
IRS REFERENCE"'!-: 11-22.5200U
SUBMISSION TYPE: New Project
ACTION: APPROVED
IRS APPROVAL DATE: September 20. 2011
IRS EXPIRATION DATE: September 19. 2012
Thank you for your recent New Project submissic•n. The Tulane University Institutional Review Board has granted approval for the above-referenced protocol together with:
Amendment!Modification Form (UPDATED: OB/19!20'11)
Application for Hum<:ln Subjects Resemch, Part 'I (UPDATED: 06;22/20'1'1)
Application Part 2 (UPDATED: 09i12i201'!)
Consent Fom1, Version Date f•il2f1l (UPDATED: 09.'12!20'1 ·1)
Initial Recruitment Script (UPDATED: 06!'13/20'1'1}
Cover Sheet (UPDATED: Of•il fii20'l'l)
• Letter to Stakeholder (UPDATED: 09ilft!20'1'l)
Letter to Expert (UPDATED: 06!'13!20'11)
Checklist for Expedited Review (UPDATED: Of•i'l 9i201·:)
lnterliew Script (UPDATED: 09!'! 5!20 1'1)
Protocol (UPDATED: Of)/! fi/2011)
Delphi questionnaire (UPDATED: O~lii 9!20'1'1)
SMART Questionnaire (UPDATED: 09i'l2i2011)
• Training/Certification- CITI Training-Completion report-Benjamin Schulte (UPDATED: 013.'23!201'1)
Training/Certification- CITI-Completion Report-Andrew Englande (UPDATED: 013i'l3/20'i'1)
in accordance with 45 CFR 413.'1'l 0 . Please note the expiration date of the protocol <Jbove.
125
Appendix B: Delphi Study Round One
Please answer these questions based upon your expertise gained following the BP Deepwater Horizon oil disaster and understanding of regional needs for St. Bernard Parish:
1. Please rate each item on a scale of 1-5 according to its importance in maximizing environmental, economic, and societal recovery in St. Bernard Parish. Circle (or bold) the best answer. 5 = strongly agree, 4 = agree, 3 = neither agree nor disagree, 2 = disagree, and 1 = strongly disagree.
2. Please list any additional important variables that should be taken into account that promotes environment, economic, and societal recovery for St. Bernard Parish following the BP Deepwater Horizon and rate those statements on the aforementioned scale.
Definition of terms
Resilience: The capacity of a system to absorb disturbance and reorganize while undergoing change so as to still retain essentially the same function, structure, identity, and feedback modify
Regional economic stability: Methods and practices that promote the economic development and growth of a region. In this research it pertains to St. Bernard Parish.
Environmental restoration: Practices that promote ecological and biological recovery following a man-made or natural disaster.
Societal measures: Efforts to promote the wellbeing and health of a community affected by a man-made or natural disaster.
126
Environment:
Removal and disposal of residual oil: Allocate resources to cleaning any oil that should surface, wash-up to shore, or invest in technology that best addresses underwater oil plumes.
Construct Oyster Reefs: Oyster reefs would promote shoreline stability and contribute to the ecosystem of Lake Borgne.
Cypress Swamp Restoration: Allocate funds to reroute the use of wastewater and freshwater to flow into the cypress swamps to mitigate the impacts of saltwater intrusion and promote cypress swamp restoration.
Prevention of Herbivory: Trapping of invasive species, primarily nutria, to reduce their impact on wetlands, therefore providing a healthier and more resilient wetland system.
Restoration of Barrier Islands: Infusion of sediment to counter the landmass lost to the barrier islands associated with erosion.
River Diversions: Use available funds for river diversions (Canarvon and Violet) in St. Bernard Parish to promote the introduction of freshwater to the marshes in St. Bernard Parish.
Sustain ability of fisheries: Conduct studies quantifying the impact of the oil leak on fish populations and analyzing the long-term impact.
127
Logistical Capacity for Long Term Recovery:
Basic Equipment and Necessities for residual oil clean-up: Boom, boats, dispersants, Personal Protective Equipment, food and shelter for workers, fuel.
Education and Training: HAZWOPER training, and other related training for programs to those forced to adjust their occupation to relevant and available work following an oil leak.
Establish monitoring system: Environmental Sensitivity Index, use of GPS, and methods that allow ease of reporting of oil sightings.
Information Sharing: Develop and implement systems for data integration, synthesis, sharing and dissemination.
Streamline Recovery Fund Procedure: Provide additional resources to community, particularly workers who can verbally describe the paperwork that is required with in applications.
Parish Staff I Parish Administrative capacity: Provide funds for additional St. Bernard Parish staff and administration to handle increase administrative needs associated with the BP Deepwater Horizon incident.
Fundingfor increased community meetings and programs: Promote understanding of all aspects of recovery and restoration of a disaster between stakeholder groups.
128
Regional Economic Stability:
Outreach Programs: Create television commercials and magazine adds to promote tourism.
Aquaculture Studies: Finance for independent laboratories to examine extent of contamination and bioabsorptionlbioaccumulation of aquaculture.
Restaurant and Hotel Subsidies: Subsidies for restaurant and hotel owners and workers to account for 100% of 5-year average profit prior to disaster.
Stipend for business adjustment to new environmental regulation: Provide money to small business that funds updating their business to meet new environmental regulation resulting from oil spill.
Loans for small business development: Increase availability of micro loans to individuals for small business use and/or start-up from affected communities.
Government funding: Provide local governments with funds to compensate reduced tax revenue as a result of reduced industry output.
Societal Impacts:
Community education outreach: Provide outreach sessions to communities that provide scientific backing on the health outcomes and effects of the oil and dispersants.
Bolster Educational Programs: Promote education and literacy in rural and urban regions.
129
--~--~-- --------~-------~---------~
Public health staffing and databases: Increase states resources for surveillance of adverse health effects associated with oil and dispersants.
Improve capacity of public health programs: Increase funds for social programs such as food stamps, school lunches, immunizations, and maternal health for effected populations.
Increase mental health capacity: Develop resources and providers to deal with mental health issues, like Post Traumatic Stress Disorder (PTSD), to communities affected by the spill. Please see Appendix A for the decision tree.
Population and demographic studies: Establish baseline data following the oil disaster to help determine future resource allocation.
Language translation: Provide language translation services to those in need.
130
Variables Strongly agree--7Strongly Disagree
Promote Regional Economic Stability
Outreach Programs ................................................. 5 4 3 2 1
Aquaculture Studies ............................................... 5 4 3 2 1
Restaurant and Hotel Subsidies .......................... 5 4 3 2 1
Stipend for business adjustment to new environmental regulation .....................................
5 4 3 2 1
Loans for small business development .............. 5 4 3 2 1
Funding for government services ....................... 5 4 3 2 1
Additional important variables
............................................................................................... 5 4 3 2 1
............................................................................................... 5 4 3 2 1
............................................................................................... 5 4 3 2 1
131
5 4 3 2 1
5 4 3 2 1
5 4 3 2 1
5 4 3 2 1
5 4 3 2 1
132
Variables Strongly agree--7-Strongly Disagree
Environmental Restoration
Removal and Disposal of Residual Oil. ................. 5 4 3 2 1
Construct Oyster Reefs ......................................... 5 4 3 2 1
River Diversions ....................................................................
5 4 3 2 1
Cypress Swamp Restoration ...............................................................
5 4. 3 2 1
Herbivory Prevention ............................................
5 4 3 2 1
Restoration of Barrier Islands ..............................
5 4 3 2 1
Examine Sustainability ofFisheries .....................
Additional important variables 5 4 3 2 1
................................................................................................
5 4 3 2 1 ...............................................................................................
133
5 4 3 2 1
5 4 3 2 1
5 4 3 2 1
5 4 3 2 1
5 4 3 2 1
134
Variables Strongly agree--?Strongly Disagree
Logistical needs for recovery
Basic Equipment and Necessities for residual oil clean-up ..............................................................
5 4 3 2 1
Education and Training ......................................... 5 4 3 2 1
Establish Monitoring System ............................... 5 4 3 2 1
Information Sharing ............................................. 5 4 3 2 1
Streamline Recovery Fund Procedure .............. 5 4 3 2 1
Funds for Increased Community Meetings and Programs ...................................................................
5 4 3 2 1
Parish Administrative Capacity ...........................
Additional important variables 5 4 3 2 1
...............................................................................................
135
5 4 3 2 1
5 4 3 2 1
5 4 3 2 1
5 4 3 2 1
5 4 3 2 1
5 4 3 2 1
136
Variables Strongly agree-~Strongly Disagree
Community recovery and development
Community Education Outreach ....................... 5 4 3 2 1
Bolster Educational Programs ............................. 5 4 3 2 1
Public Health Staffing and Databases .............. 5 4 3 2 1
Improve Capacity of Public Health Programs ..................................................................
5 4 3 2 1
Increase Mental Health Capacity ......................... 5 4 3 2 1
Population and Demographic Studies .............. 5 4 3 2 1
Language Translation ........................................... 5 4 3 2 1
Additional important variables
........................................................................ ········· .............. 5 4 3 2 1
137
····-·--·-------·-··- -------------·
5 4 3 2 1
5 4 3 2 1
5 4 3 2 1
5 4 3 2 1
5 4 3 2 1
Comments:
138
Appendix C: Delphi Study Round Two
Please answer these questions based upon your expertise gained following the BP Deepwater Horizon oil disaster and understanding of regional needs for St. Bernard Parish:
3. Please reconsider your own previous rating and that of the entire panel (the Round 1 group mean and standard deviation).
4. Rate each item on a scale of 1-5 according to its importance in maximizing environmental, economic, and societal recovery in St. Bernard Parish. Circle (or bold) the best answer. 5 = strongly agree, 4 = agree, 3 = neither agree nor disagree, 2 = disagree, and 1 = strongly disagree.
5. Please list any additional important variables that should be taken into account that promotes environment, economic, and societal recovery for St. Bernard Parish following the BP Deepwater Horizon and rate those statements on the aforementioned scale.
Definition of terms
Environmental Variables: Measures that promote restoration and ecological recovery following an oil related incident.
Economic Variables: Methods and practices that promote economic development and growth in St. Bernard Parish following an oil related incident.
Logistical Capacity for Disaster Response Variables: Measures that promote the operational capacity to handle future oil related incidents.
Societal Impacts Variables: Measures to promote the well-being and health of communities following an oil related incident in St. Bernard Parish
Mean: The mathematical average of all scores submitted for each variable
Standard Deviation (Std Deviation): A statistic that shows the spread or dispersion of scores for a particular variable. The more widely the scores are spread out, the larger the standard deviation. The lower the score, the stronger the agreement.
139
Example: A standard Deviation of 0.5 reflects a stronger agreement in response than a score of 1.0
Please complete and return round 2 questionnaire by September 13th, 2011
If you have any questions, or concerns, please feel free to contact me at 312.402.4704 or Thankyou
Environment:
Removal and disposal of residual oil: Allocate resources to cleaning oil that should surface, wash-up to shore, or invest in technology that best addresses underwater oil plumes.
Construct Oyster Reefs: Oyster reefs would promote shoreline stability and contribute to the ecosystem of Lake Borgne, St. Bernard Parish.
Cypress Swamp Restoration: Allocate funds to reroute the use of wastewater and freshwater to flow into the cypress swamps to mitigate the impacts of saltwater intrusion and promote cypress swamp restoration.
Prevention of Herbivory: Trapping of invasive species, primarily nutria, to reduce their impact on wetlands, therefore providing a healthier and more resilient wetland system.
Restoration of Barrier Islands: Infusion of sediment to counter the landmass lost to the barrier islands associated with erosion.
River Diversions: Use available funds for river diversions (Canarvon and Violet) in St.
Bernard Parish to promote the introduction of freshwater to the marshes in St. Bernard Parish.
140
Sustain ability of fzsheries: Conduct studies quantifying the impact of the oil leak on fish populations and analyzing the long-term impact.
Logistical Capacity for Long Term Recovery:
Basic Equipment and Necessities for residual oil clean-up: Boom, boats, dispersants, Personal Protective Equipment, food and shelter for workers, fuel.
Environmental Health Education and Training: HAZWOPER training, and other related training for programs to those needing to adjust their occupation to relevant and available work following an oil leak.
Establish monitoring system: Environmental Sensitivity Index, use of GPS, and methods that allow ease of reporting of oil sightings.
Information Sharing: Develop and implement systems for data integration, synthesis, sharing and dissemination.
Streamline Recovery Fund Procedure: Provide additional resources to community, particularly workers who can describe the paperwork that is required with in applications.
Parish Staff I Parish Administrative capacity: Provide funds for additional St. Bernard Parish staff and administration to handle increase administrative needs associated with the BP Deepwater Horizon incident.
Funding for increased community meetings and programs: Promote understanding of all aspects of recovery and restoration of a disaster between expert groups.
141
Regional Economic Stability:
Economic Outreach Programs: Create television commercials and magazine adds to promote tourism.
Aquaculture Studies: Finance for independent laboratories to examine extent of contamination and bioabsorptionlbioaccumulation of aquaculture.
Restaurant and Hotel Subsidies: Subsidies for restaurant and hotel owners and workers to account for 100% of 5-year average profit prior to disaster.
Stipend for business adjustment to new environmental regulation: Provide money to small business that fund updating their business to meet new environmental regulation resulting from oil spill.
Loans for small business development: Increase availability of micro loans to individuals for small business use and/or start-up from affected communities.
Government funding: Provide local governments with funds to compensate reduced tax revenue as a result of reduced industry output.
Societal Impacts:
Community education outreach: Provide outreach sessions to communities that provide scientific backing on the health outcomes and effects of the oil and dispersants.
Bolster Educational Programs: Promote education and literacy in rural and urban regions.
Public health staffing and databases: Increase states resources for surveillance of adverse health effects associated with oil and dispersants.
142
Improve capacity of public health programs: Increase funds for social programs such as food stamps, school lunches, immunizations, and maternal health for effected populations.
Increase mental health care capacity: Develop resources and providers to deal with mental health problems, like Post Traumatic Stress Disorder (PTSD), to communities affected by the spill.
Population and demographic studies: Establish baseline data following the oil disaster to help determine future resource allocation.
Language translation: Provide language translation services to those in need.
143
Strongly Agree 7 Strongly Std
Variables Disagree Mean Deviation
Promote Regional Economic Stability
Outreach Programs 5 4 3 2 1 4.00 1.00
Aquaculture Studies 5 4 3 2 1 3.80 0.941
Restaurant and Hotel Subsidies 5 4 3 2 1 2.93 0.798
Stipend for Business Adjustment to New Environmental Regulation 5 4 3 2 1 3.86 0.915
Loans for Small Business Development 5 4 3 2 1 4.33 0.899
Funding for Government Services 5 4 3 2 1 3.46 1.06
Additional Variables Suggested by Experts - - - - - - -
Oversight of the distribution and use of funds 5 4 3 2 1
....... ,, __ , -Economic development plan to enhance and build additional business opportunities
5 4 3 2 1
·-····------
Additional Variables - - - - - - -.............................. ___ ,,_, __________ ,
5 4 3 2 1 ............................ _, __ , ___ ,_,_,,.,, __ ,, ___ .,,_,, _______ ,.,_,_,,_,,, ____ , ___
5 4 3 2 1
·---····--· ... - ..... _, __ , ·-----·-· --5 4 3 2 1
................. , ___ ,., __ ,, _____ ,, _____ , ____ ------
144
Comments:
145
Strongly Agree 7 Strongly Std
Variables Disagree Mean Deviation
Environmental Restoration
Removal and Disposal of Residual Oil 5 4 3 2 1 4.16 0.937
Construct Oyster Reefs 5 4 3 2 1 4.20 0.861
River Diversions 5 4 3 2 1 4.46 0.967
Cypress Swamp Restoration 5 4 3 2 1 4.73 0.457
Herbivory Prevention 5 4 3 2 1 3.86 0.862
Restoration of Barrier Islands 5 4 3 2 1 4.33 0.975
Examine Sustainability of Fisheries 5 4 3 2 1 4.40 0.828
Additional Variables Suggested by Experts - - - - - - -
Funds for Research Impacts of Rising
Energy Costs 5 4 3 2 1
··--·· Funds for Researching Impact of Climate
Change 5 4 3 2 1
····--···--·"-"' ____ ··-Monitoring natural attenuation of residual
oil 5 4 3 2 1
--··-·---·--------------------·----· Additional Variables - - - - - - -
····-····-··--····---
5 4 3 2 1
............. , .. ,_,_,_ .......... ··------·---
5 4 3 2 1
··-·-··-·-··· -· , __
146
Comments:
147
Strongly Agree~ Strongly Std
Variables Disagree Mean Deviation
Logistical Needs for Recovery
Basic Equipment and Necessities for Residual Oil Clean-Up 5 4 3 2 1 3.86 1.060
Education and Training 5 4 3 2 1 4.13 0.833
Establish Monitoring System 5 4 3 2 1 4.06 0.883
Information Sharing 5 4 3 2 1 4.33 0.816
Streamline Recovery Fund Procedure 5 4 3 2 1 4.46 0.767
Funds for Increased Community Meetings and Programs 5 4 3 2 1 4.00 0.925
Parish Administrative Capacity 5 4 3 2 1 3.60 0.910
Additional Variables - - - - - - -
5 4 3 2 1
......................... ___ , .. - -
5 4 3 2 1
-·-·-·-····-·----------·----··----··--- ,,_,_
5 4 3 2 1 _,_,,_,,_,_,,,,,.,_,,., ____ , ____ ,, _________ ,_,_,_, ___
5 4 3 2 1
Comments:
148
Strongly Agree 7 Strongly Std
Variables Disagree Mean Deviation
Community recovery and development
Community Education Outreach 5 4 3 2 1 4.40 0.828
Bolster Educational Programs 5 4 3 2 1 4.40 0.632
Public Health Staffing and Databases 5 4 3 2 1 3.86 0.833
Improve Capacity of Public Health Programs 5 4 3 2 1 4.06 0.798
Increase Mental health Capacity 5 4 3 2 1 4.13 0.639
Population and Demographic Studies 5 4 3 2 1 3.66 0.975
Language Translation 5 4 3 2 1 3.66 0.723
Additional Variables Suggested by Experts - - - - - - -
Improve Medical Care Services 5 4 3 2 1
-----Train Healthcare Providers about Health Effects Related to Oil Spill 5 4 3 2 1
···---· _, __ ,, Additional Variables - - - - - - -
~'''''-'''"'''"-"""'M_,,_, __ , __ ,_,, ____ ,,, _____ ,_,, __ , __ ,,_,,_,, ___ ,,_,
5 4 3 2 1 ................... _,, ___ ,, ___ ,,, __ ,,_,,,,,_, __ , __ ,,_. _______ , ____ ,
5 4 3 2 1
,,_,,,,, ____ ,_,,
5 4 3 2 1 ............... , .... _,_, __ , ___ , __ ,,_,, __ , __________ , __ ,, __________
Comments:
149
Appendix D: SMART Questionnaire
Resource Allocation Following the Deepwater Horizon Event
Part 1
Please rank the variables listed below based on their relative importance to RESOURCE
ALLOCATION FOLLOWING THE DEEPWATER HORIZON EVENT AND LONG TERM RESILIENCE IN ST.
BERNARD PARISH, LA. Consider that all variables are presently set at their lowest level and
select their order of importance from the lowest level to the highest level.
For example, the variable that should be improved first, or for which resources should be
allocated first, should receive the rank of 1. Continue this process until all variables are ranked.
ENVIRONMENT
LOGISTICAL CAPACITY FOR DISASTER RESPONSE
REGIONAL ECONOMIC STABILITY
SOCIETAL IMPACTS
Next, rewrite the ranked variables in ascending order (the variable ranked #4 should be written
at the top). Note that although it is the 4th ranked variable it is placed in the first position on the
list. The 4th ranked variable is assigned the value of 10. Assign a value to the 3'd ranked variable
that shows how much more important it is to improve the 3'd ranked variable in relation to the
4th ranked variable.
For example, if improvement ofthe 3'd ranked variable is 3 times more important than
improvement of the 4th ranked variable, then the value of 30 should be assigned to the 3'd
ranked variable. Continue this rating in pairs until all variables are rated.
Rank Variable
_10_
150
Part 2: Environment
Please rank the variables listed below based on their relative importance to the ENVIRONMENT
AND THE LONG-TERM NEEDS OF ST. BERNARD PARISH, LA FOLLOWING THE DEEPWATER
HORIZON EVENT. Consider that all variables are presently set at their lowest level and select
their order of importance from the lowest level to the highest level.
For example, the variable that should be improved first will receive the rank of 1. Continue this
process until all variables are ranked.
REMOVAL AND DISPOSAL OF RESIDUAL OIL
CYPRESS SWAMP RESTORATION
CONSTRUCTION OF OYSTER REEFS
RIVER DIVERSIONS
RESTORATION OF BARRIER ISLANDS
HERBIVORY PREVENTION
EXAMINE SUSTAINABILITY OF FISHERIES
FUNDS FOR RESEARCHING IMPACT OF CLIMATE CHANGE
MONITORING NATURAL ATIENUATION OF RESIDUAL OIL
Next, rewrite the ranked variables in ascending order (the variable ranked gth should be written
at the top). Note that the gth ranked variable is listed first and assigned a value of 10. Assign a
value to the gth ranked variable that shows how much more important it is to improve the gth
ranked variable in relation to the gth ranked variable.
For example, if improvement of the gth ranked variable is 3 times more important than
improvement of the gth ranked variable, then the value of 30 should be assigned to the gth
ranked variable. Continue this rating in pairs until all variables are rated.
151
Rank
_10_
Variable
152
Part 2: Logistical Capacity for Disaster Response
Please rank the variables listed below based on their relative importance to the LOGISTICAL
CAPACITY FOR LONG-TERM RESILIENCE OF ST. BERNARD PARISH FOLLOWING THE DEEPWATER
HORIZON EVENT. Consider that all variables are presently set at their lowest level and select
their order of importance from the lowest level to the highest level.
For example, the variable that should be improved first should receive the rank of 1. Continue
this process until all variables are ranked.
BASIC EQUIPMENT AND NECESSITIES FOR RESIDUAL OIL CLEAN UP
EDUCATION AND TRAINING
ESTABLISH MONITORING SYSTEMS
INFORMATION SHARING
STREAMLINE RECOVERY FUND PROCEDURE
FUNDS FOR INCREASED COMMUNITY MEETINGS AND PROGRAMS
PARISH ADMINISTRATIVE CAPACITY
Next, rewrite the ranked variables in ascending order (the variable ranked 7th should be written
at the top). Note that the 7th ranked variable is listed first and is assigned a value of 10. Assign a
value to the 6th ranked variable that shows how much more important it is to improve the 6th
ranked variable in relation to the ih ranked variable.
For example, if improvement of the 6th ranked variable is 3 times more important than
improvement ofthe 7th ranked variable, then the value of 30 should be assigned to the 6th
ranked variable. Continue this rating in pairs until all variables are rated.
Rank Variable
_10_
153
Part 2: Regional Economic Stability
Please rank the variables listed below based on their relative importance to the LONG-TERM
ECONOMIC STABILITY OF ST. BERNARD FOLLOWING THE DEEPWATER HORIZON EVENT.
Consider that all variables are presently set at their lowest level and select their order of
importance from the lowest level to the highest level.
For example, the variable that should be improved first should receive the rank of 1. Continue
this process until all variables are ranked.
FUNDING FOR GOVERNMENT
OUTREACH PROGRAMS TO PROMOTE TOURISM AND SAFETY OF FISHERIES
AQUACULTURE STUDIES
STIPEND FOR BUSINESSES ADJUSTMENT TO NEW ENVIRONMENTAL REGULATION
LOANS FOR SMALL BUSINESS DEVELOPMENT
Next, rewrite the ranked variables in ascending order (the variable ranked sth should be written
at the top). Note that the sth ranked variable is listed first and assigned a value of 10. Assign a
value to the 4th ranked variable that shows how much more important it is to improve the 4th
ranked variable in relation to the sth ranked variable.
For example, if improvement of the 4rd ranked variable is 3 times more important than
improvement of the sth ranked variable, then the value of 30 should be assigned to the 4th
ranked variable. Continue this rating in pairs until all variables are rated.
Rank Variable
_10_
154
Part 2: Societal Impacts
Please rank the variables listed below based on their relative importance to SUPPORT THE
LONG-TERM COMMUNITY RECOVERY AND DEVELOPMENT IN ST. BERNARD PARISH FOLLOWING
THE DEEPWATER HORIZON EVENT. Consider that all variables are presently set at their lowest
level and select their order of importance from the lowest level to the highest level.
For example, the variable that should be improved first should receive the rank of 1. Continue
this process until all variables are ranked.
COMMUNITY OUTREACH AND SUPPORT PROGRAMS
BOLSTER EDUCATIONAL PROGRAMS
PUBLIC HEALTH STAFFING AND SURVEILLANCE DATASBASE
IMPROVE CAPACITY FOR PUBLIC HEALTH PROGRAMS
INCREASE MENTAL HEALTH CAPACITY
POPULATION AND DEMOGRAPHIC STUDIES
LANGUAGE TRANSLATION
IMPROVE MEDICAL CARE SERVICES
Next, rewrite the ranked variables in ascending order (the variable ranked gth should be written
at the top). Note that the gth ranked variable is listed first and assigned a value of 10. Assign a
value to the ih ranked variable that shows how much more important it is to improve the 7th
ranked variable in relation to the gth ranked variable.
For example, if improvement of the ih ranked variable is 3 times more important than
improvement of the gth ranked variable, then the value of 30 should be assigned to the ih
ranked variable. Continue this rating in pairs until all variables are rated.
155
Rank
_10_
Variable
156
AppendixE
Simple Multiattribute Rating Technique Raw Data
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0,0192
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l f)_(rJ72 1 (1~3·~r:.6 1 0.1242 t OT72<i f lj(I'J21 l (1_(1186 1
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K1 o,oo::n ,!<) 0.0138
Jl.i') 0.093.5 .j(W) 0.2339
10 0.0058
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I 6.~J 0,2819
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10:00 0,4405 .j(l 0.0176
l({l 0.0704 ll;:l 0.03.52
RAW DATA
BUSINESS H.£1'lU:S£NrATJVES £XI'£R'fGROUJ•
F..~JI'I'rl 10 I F..1:JI<Ort II l:•Jl<'ri 12
ObJ«th~ I Rant Raw Wei&bt I R.&o}; Raw Wcisbt Rant Raw W~.i!:bt I £nvironmet~w I 2 tKl 0.2'158 I 2 10 (1,]794 2 ;i(l 0.2500 1
.Losi!tkal Ca.pacily t<.• Di<.,qer R<:':IJXJn!e l 3 ·10 0.1379 1 .t 10 OJJ256 3 20 (U666 1
Regioru1 f.;ooonm Slabili1y I I lW 05517 I I 280 0,?)19 I (£) 0,.5000
Societallnopo!!:.tl l 4 10 (1.03# l 3 30 O.o769 4 10 OJI833 1 I I 1
Rtt~tova.l ;~.nd Dilpo:!al of lt~dual Oil .l 4 66 0.122':) 1 1 280 02641 8 10 0.0400 1 Cy{l'e:H Swat~tp Rt:i!etttion l 2 66 0.122'1 I 3 280 0.26-tl 3 ,1(1 C:U600 I CQn<lr\lc.ti<., (11" Oyllle< ~~~ I I 26-t O..t916 1 7 3(1 0.0283 2 60 O.Z.IOO 1
f:11vir<mmental 11.1,.... Dn•<r.li<JI'" I s 10 0.0186 1 9 10 0.009-1 5 20 o.OSOO 1 Re~.,n.tJao (11" I~ I~-~~ I :3 66 0,1229 1 2 280 0.2641 4 :10 0.1200
Hcrohoory.l'rC\-:DiioD I 6 22 o 0409 1 5 so 00471 ' 6 10 0.0400
1-' Fw:nil'l:' S•Jstam&bility (lfFish=rie! I 9 10 0.0186 I 6 30 0 02.83 I 1 ((I 0.24(1J 0'1 H.e:3c-ardlifl8 bol~:l .:.d'C'liroLa1e <.'lo8f18e I 7 II 0.(1204 I 8 30 0 .028:) l 9 10 0.0400 1-'
Motoit:Jrins }kruraJ At~etow.tioo of Jte!idw.l Oil I 5 22 o.~lo9 I " 10 0.0660 1 7 10 O.Q.\00
I B:l:!l~ Equipmftll for J~doJ;l] Oil Clwl Up I 2 120 0.3428 I 1 280 0.5714 1 5 JO 0.0571
fdoJe;s.ti.:~o an:t T runir•s I .j 2(1 0.0571 1 .s 20 0.~108 1 1 !f.) 0.2857
E:ll&blillo~\toni!orins SyJioffil I I 120 03'28 I 2 70 0J.j28 I 3 :;.o 0.11].1
I.Qsi,.:ti...i hof(nll41km Sbaring I 1 lO o.o2!!5 1 :3 50 0.1020 1 2 .\5 0.2571
S1reamlone Ro::"~ Fton11'rQCechore I :3 (£) 0.1714 1 4 40 00816 l 4 w 01142
Fnoos f•Jr JuQ~ C«nmunity MeetinS~IPrograma I 5 10 o 02!!5 1 6 20 0.0408 1 6 10 O.a571
Parish ,\dm.inil1r&li¥e D.pa..11y I ti lO 0.02!!5 I 1 10 0.0204 I 7 10 0.0~71
I Fun:liro8 fur Gc•vemmenl l 5 10 0.(1357 I 5 10 0.0025 $ 10 0.(17ti9
0Ulrtadl1'ro8f.ltl~ to l'romalo T ouri!fiLq;·i:lbcrie::l I 4 10 o.mn 1 3 60 0.015.S 2 :;.o 0.2307
)'.c(ln1ml;; Aqu~ture :S!u:li<:':l l 3 2(1 0.07].1 ·I 20 0.0051 3 2(1 0.1538
S1ipen1 t<. llu<in""' Adju$men1 •(1 N~ l'nviro R<;siJIA1iP. 2 80 0.2857 2 180 0.046S ,, 10 0.0769 1 1.,..,~ !(or Small ~$inc.,. Dev~pnenl I I ](£) 05714 I 3(£)0 0.9302 I (£) 0.4615 1
I l Community Ol11r~ w Suppon Programs I 4 120 0.08a5 3 50 0.0925 6 10 0.0476 1 Il<1l:!a .f::du~oo Progr;\1113 I 5 120 0.(11105 6 30 0.0555 1 ((I 0.2857 I l'ubl~ Health Slilffirl!; u.:l Stonodllan:e :Uiitibil:ll:: I 3 3ro 0.2416 4 50 0.0925 5 10 0.0476 1
Socicul ll!LprOYc CJ~pad1y for 1'11b~e Health J'rosran1:1 I 1 360 0.2t 16 I 280 0.$1 !!5 " 2Q 0.0952 1 bo~ MesJto.l llealth C~acily I 6 120 o.osas 2 70 0.1296 3 :l(J 0.1-128
PoJKihi~Jn .s:nd Demo gat~ lee Stll<lje.! I 7 .j(l 0.0268 7 20 0.037() 1 10 0.~176
l.Of1;!!tJ.1:!ieTramiul~ I l! 10 0.0067 5 30 O.a555 8 10 0.0476
lmJlrQVe Me>:!kal Sen1CC:! I 2 3(£) 0..2416 8 lo 0,01.!!5 2 (£) 0.2857
.R,\WD,\TA
SCIENCE AND TECHNICAL REPRESENTATIVES EXPERT GROUP I uJl~rt 13 1 E>J•"'rl 14 1 t:.qo~rt 15
Objecth"C• i Rank Raw We\'!b!l Rank RAW WdshC I ll.W: R~w Wei.!;hl I f:nviromnC!It!l I I 4() 04210 1 J 12 0.1875 1 I 300 o 769~ 1
Loginical C~i1)• for Diwa Respomc I 4 10 o 1052 1 I 27 04218 1 3 20 o os12 1 ltesioml Ecoocwnk Sut.Dit~ I 2 3(1 OJIS'I 1 2 15 01:>43 1 2 60 O.LHS I SociotillmpaGH I 3 15 0.1578 1 4 10 0.1.562 1 " 10 0.02.56 I
I RemQV&I AJid Di•JIO~I QfRe~xl~ Oil I I 600 035!/J 1 2 IO&J 02070 1 I 11520 o_~"N 1
C)'prCSII Swamp Rcalor.Wm I 6 45 (1.0266 1 7 45 0.0086 I 4 %0 0.04:"9 I Coo:llndion of()yg!ef Rcefl I 9 JO 0.0059 1 6 ~ 00172 1 9 10 0.0004 1
£to vironfllffilaJt.ilu Di \'ttl Joru l ,, 115 O.o79S I 8 30 o.oon 1 7 6(1 o.oo27 1 Jle'II(Q.Iion ofll.arrler bland~ I 5 ~ O.OS:u 1 I 2160 O.H\1 I ::; 2880 o.1319 1 HerniYc.y ~tim I 8 20 o 0118 1 9 JO 00019 1 g 30 0.0013 1
Exanine Smlaimbuity ofFish erie;! I ::; 250 0.14'19 1 4 540 0.103.5 1 6 120 O<IJS4 I
Rc:le;lfclUrJ8 lmp:l¢t<l of Climale C11a:tl8e I "' 40 o 02!<i I ;; JO&:i 01070 1 s ~0 00219 1 I-' MQniC(WillB Natur&l ,\aenualiJn of Re~xhu.l Q~ I 2 .500 0.2951! I .5 IW 0.03-1.5 1 2 .5760 0.2639 I en N I I I I
B&iiw EquiJIIlc:nlforResidw.l Oil Clean Up I I 220 o 4230 1 .1 28&3 0.6956 ! 6 20 0 0023 1 £duca1ioo a:nd Ttainio8 1 4 4.5 0.0865 1 4 120 0.0289 1 2 ~0 0.0566 1 E:llabli!h Mooitain,g Sy:lle~u I 2 110 0211.5 1 s .\0 0.0096 1 .5 40 o.00·\7 1
I.CJsi<~>=-•1 lnl<nn41l:ln Slwill8 l 3 ~ 0.17JO 1 2 720 0.1739 1 I 7680 <L~67 1
Slrea.mline Rcwvecy fm)j Pro~ure I 6 15 0.0288 1 3 360 0.0869 1 3 160 0 OISB 1 Funds fct" lncreA-!ed Community }.f~:ings,'l'Jograms I 7 10 0.0192 1 6 10 O.OOH 1 4 80 000~ I
J':!ri;sb Adrn.ini:llratil<e Cap!city I 5 :!<) 0.05"16 1 "' JO 0.0024 1 7 10 o <IJil I j
Funding Ia f',ovenm1<;:ni I 2 4() 02285 l 2 30 02.5()0 1 2 180 0.1800 1
Oulreacll ProgrUI13to Prcmote Tourism/rl;!bcrica l 3 30 0.1714 1 5 10 00833 5 10 o 0100 1
Eron.lnti:; Aqu&<:<~ltute Stud~ l 4 ~~ O.OSS'I 1 4 15 0.1250 I 720 0.72(1J 1
Stjpro:l for lluiioc-.H Adjwiru.C1Jito New Erll'iW ltc-guht~Jtl .5 10 (J.0.571 l 3 2.5 02083 3 60 o.()5oo I J.~~ f(l" Small llu<ine<!.l ~elcJJ-JIC!It I I 80 0.4571 1 I ,t(J .0.3333 ·i 30 0.0300 l
I I I Community Oil tread! and SllJ1la1 Programs I I 64:8 0.4544 I 6 20 0.0990 2 %0 03855 I
l:lolncr Edu:att:.tJI'ropn:3 I 6 !<i 0025:2 1 3 3(1 0.1~5 I %0 0.3855 1 l'llblic lle4lfo liallill8 &~~d 1hJTYcilhn:e ll1.1"1"'1e I ·I !OK O.<J7.57 I .5 22 0.1089 6 60 0.02·10 1
li{ICieal JmpmveCap&citylor!'llbli; lie&ilh l'wgn.m• l 5 71 O.OSGI I 7 15 0.0742 •7 20 O.OO&J I
lntrca.1C Mc:o!a.l Hcaltb C~ity I 3 216 0.1514 1 I 4S 02227 g 10 o 0040 1
Populatim and Dmloga.pbiw Stu:lk! I 1 12 O.((IS' I 4 24 O.lJSS 3 240 0(1963 1
L&n,gll~<e 'f fa:tll!Mioo I 8 10 0.0070 1 8 10 0.(1.\9.5 " 120 o.t~t81 1 lmJirove Mocli:al Servl:e, I 2 32-i 0.2272 1 2 36 0.1782 .5 120 O.OiSi I
Jl,AW.DATA
SCJENC£ AND 'f £CHNICAL JU:l'JU:SENr A: riVES !OXI'!ORT GltOUI' I F:<JH!rt I~ I E•11crt 17 I E'Jlert 18
Objmlvt<~ I R&n~ RAw '>'r'cisld I Rank RAw Wcighj I Rank RAw Weisld I Fn¥ironmem&l I 2 200 (I 0896 I l 4SO 0.7758 1 1 225 06250 1 Loj;i!lli:al Capl.city for Di>Wb'ltespooile I 3 20 o.oos9 1 ~ 10 0.0112 1 3 50 0.13118 1 Re;!;i<W~&li!C(I'I(micSiabi~ly I 4 10 0.0044 1 3 :30 0.0512 l 2 75 O.W&l I So;.ieallm~s I l 2(100 o 8968 1 2 90 0,1551. 1 4 10 o 0211 1
I I 1 H.tlltovalud Di!lp<>lal of Jtc:3idwl Oil l 1 45000 088U 1 l ({.0 0.326? 1 1 2400 orou 1 CY!'~ S~nJl Jl~.,r&ti(l!l I 5 ro 0.0011 1 2 660 0~267 l .t 120 O.OiOO I Cmse.,ci<)n<:d'Q)'!l11;< .1~13 I 6 20 o O<J:l3 I 8 30 0.0148 1 :3 120 o 0400 t
Fnviroomema!RiYcr Dh=iom I 8 lO 0.0001 l ' 160 0.0792 1 6 20 0.0066 1 R~-tiooofflurlu hluldl l 7 20 0.0003 1 1 .to <L0198 t 5 60 0.0200 1 1b'lliv<-Y l'r~tim I 9 10 (U)001 I 9 10 o.oow 1 9 !0 0.0033 1 Examine Swn.imbility of Fisheries I 4 ISO 0.0035 1 6 80 003'96 1 2 240 o 0801 1
1-' Jlt:!eatd!ius Impact! ofC1ima1o Dl!fl~ I 3 900 o.om 1 3 220 01089 1 7 15 0.0050 1 en w Moo~oon,g Nil ural AaC1111Mioo of JWdual Oil I 2 .uoo o.o887 1 4 160 0.0?92 1 8 10 0.0033 1
i I 1 DMio-. Equipmmj fa RCIIi:hLll Oil C1:an Up I 3 12000 o.o089 1 ? 10 00081 1 2 100 0.1724 1 lli.catioo ud Traioin,g I 2 120000 0.0899 1 6 30 0.0245 1 4 50 0.08(o2 1 l'.<nllli~ M()Oiit~ns Sy:ll~n I I 1200000 0.8999 1 5 60 0.0491 1 I 300 0.5172 1
Logis~ko&l Jnfonnatirn Shu:iii!J I 4 1200 0 0080 1 I 400 O.J278 1 3 100 0.1724 1 Slream]inc Recovery Fmd Pro.-.cdorc I {; :!>:) 0.0030 I 3 180 OJ4?5 1 ~ 10 0 0172 I J'Wldl for loorwed C(JIIIfltunity 1fectin,g~'l'r()8t&Otl I 7 10 o.oooo 1 2 360 02950 l 6 10 (1.0172 1 f>arilh AdminisCn.lh'<: Ca.p.wily l 5 lXI o.oooo 1 •I 180 0.1475 1 7 10 0.0172 1
I ! I l'Wldilll! for Ga l'ffllttlt'll1 I 5 JO 0.0243 1 1 21:)0 0.6250 1 2 JOO 04J(o6 1 Qu~ I'rq;nm1., l'rornQir: Tg,ai!iln.'l'i•hori~ I 3 :;.:, o.o731 1 ~ 50 O.IOil 1 3 20 0.0!133 1
r:c.on..1mic 1\q\UCllhue St•-1):, l I :300 0.7317 I 2 100 02083 1 I 100 o 4166 1 Stipmd for ~siOCIIs Aqjwlmmt .I.J New Enviro Regul.\1~n 4 JO 0.0243 I 5 lO 0 0/XI8 l .~ 10 o 0416 I Loaru for S(Jia.IJ flwint:i! DeveJopntetd I 2 00 0.1·163 I ~ 20 0.0~16 1 ~ 10 O.Otl6 I
I I I C.oounuoi~· Ouir=b Mid Suppm Programa I 4 640 0.0017 1 5 60 o o355 1 3 40 0.1025 1 Bols~cr &location Progr8ill;l I 6 40 0 OO:H I 4 120 0.0710 1 4 ~0 o O?(f) 1 J'ubtic lfthb Staffing M~d Surveilh.oce Ll:!.tawe I 2 32000 o.owo 1 2 360 0.2130 1 2 60 0.1538 t
S~eb.l lmprQ\'C Capa<>lly f{l- 1'1•111); ll~a.lO. l'rQSr&lll" I J 6400 o o 178 1 3 :360 02130 1 6 20 o 0512 1
I=e-4.1c MemaJ lbJG:I C~il)' I I 320000 0 S9J7 I I 720 0.4260 1 I ISO 04615 1 l'opllli!Aior• an:~ Detn(J8ra:fil~ Stu:li~ I 8 ](I 000001 6 40 0.0236 1 7 20 0.0512 1 ]Mill"~ Tran•l>1:1.~n l 7 10 0.0000 1 1 20 0.0118 1 8 10 O.o256 I 1mJW<J\'C M«<ai S<:rVl;c;:s I 5 160 oooo4 1 8 10 0.0059 1 5 30 0 07W 1
Jt,\WDATA
SCIENCE AND TECIU.lJC~\L REPRESENT ATlVES EXPERT GROUP 1 f.>.J•erllll
Objecth·"" I )! ... ).; naw We\shll Envirmmemal l 1 1»:1 O.M2B I Logisl:i.:al Capi.;ity for ])jsa;;a Rcspmse I 3 30 01071 1 Jtcsiooa.l Eooooolic Stability I 2 60 02142 1
S<.>Oi~1DIJI'IC1~ I ~ ]() 0.0357 1
I Remava.hll<i 1:>i:~~~~a1 gflle-~>.1w.1 QlJ I j; 2(J O.OtOJ; I Cypreaa Swamp Rt:!tor&1iro I 4 4Q OOS16 1
Cooslrn:ixm ofO)~Iel Rccfu I 3 ro o.l6n 1 flll'it.:IOotffi!alltivct Dhoet>~iora I 1 I~ 0~6S I
R<=!l(llll.tX.n of ll,vri<" l•lm<l• l 5 4Q O.OJ; 16 1
Heroiv~ Pmoealiro I 9 10 0.0204 1 Examine Smwmb~ity of Fisheries 1 2 ro O.J6n I l~ingbnp!!:ti ofC!irna1e C'hulge l 6 4(l 0.0816 1
!--> Moo~c:w-ina N#IIT&1 /l.llo:>t~~&lion or .Resi!ha&1 Q~ I 7 20 O.OtOJ; 1 en +::> 1 l
Baolic fquipmall fc.- lte.~ldwl Oil Clt.ul Up I 5 20 <t<IS(J9 I fdu::o1iioo at~d 'fraioios I 3 ,1() 0.11~ 1 1'.1t.bli"" Mmltori"8 System I 1 &:) 0.3178
Logisl:i.:al lofonna.tim Sl:wiD8 I 2 4Q 0.1739
SII'Ol:l•tline llecoVC1Y Fund l'roctdu.to I 6 20 0 _(18(19
J-"undl fc.- ln.::ca:~~ Cmnmunity M~D8s'1'rogtarn.!l I 4 2() 0.01;69
P....Uh Administrath..; C&~ty I 1 10 o 04:34 I
I Fundios fc.- Go\'Cmratud I 3 20 0.1176 l Ou~ l'rog~111;1 l'roo10il: T<JIIri,..lfi'J.<I...-i~ I l &:) O.·t?OS l
f..:.<ln..,..U, Aq1=uhure S ludk! I 2 4Q 0.2352 1
Slipendfor Busines~ Aqjmvn~t to Nc:w EI!Viro Reguht)tn s 10 o OS8!1 I Lollftl fc.- Small Bwioe>U De~lopoiMI I 4 20 0.1176 1
I Canm11nity Ou~ andSIIJIII<W1 J'rt)~L1 I 4 4Q o 12!(1 1
Bolsle:r Edooatxm Prognw I 6 20 0.0625 I l'ublic liea.ht• Staffins a11d SurvdiJ.u,~ Daia.l:we ! 5 4(l o.mo 1
1)(10i$l 1m1.-o~ Cq~ 1<.- l'uhli:; 1 bill l'rogram. l l &:) 0.2500 1 1n=&c<e Mem.l 1 bl" Cqncity I 3 4Q o 12!(1 1 Popul&1im an:! Dcmogr&Jili; Stu.:liea I 7 10 00312 1 WSIJ* ·rwal11.ion I 8 10 0_(1312 l
)mJI'o'..; Med>:.J S<"V~ I 2 ro 0.2500 I
RAWDAT.\
COMMUNITY llASI:JJ REI'RBSE!ff 1\TIVf.S I':Xl'ERT GROlJI'
E1J!erll0 I Ex penH 1 r.xpen n O~jedlvto:S I Ramk Raw Wcigbt I Rani: Raw Wcigllll Rank RAw Wei81d 1 l:llvitoollltfdal I I I (I O.OSOO I 1 50(1 0.7936 ' I 66 0.«155 1 Lagi:llnl Ca~ity fQr DiM.111:2" Re:!jlm~ I 2 .j(J 0.2000 1 4 ](I 0.0158 l 2 22 0.2018 1 Regimal Ecoocmic Sl&bilily I 3 5IJ 02500 l :3 40 o 0634 1 3 11 o 1009 1 S«:ielilllm~s I 4 100 0.5000 1 2 8(1 0.1269 1 4 10 0.00!7 1
I I Retna~al81ld Ui:lp(!lal afR~JidUil Oi1 I 8 to 0.02t9 l 9 to 0.0(113 1 7 13 0.0285
Cypre<l S""""l' R~t1rati1U1 I I too 02197 l 3 160 0 I t76 1 _I _192 0.4210
Coostro;l:klo ofO)'.ller Reeu I 3 70 0.1538 1 5 40 0 0294 1 4 44 00964
fmiroontttdal ltiYtt Di...wi:Jw I 2 100 01197 1 I 500 OJ67(i 1 3 48 0.1050
l~cr&iionofl:lurinlila•xb l 7 1S 0.(629 l ·I 80 0.051!8 1 s 22 0.~182
lkotiv(ll)' ""'-tiQO I 4 ro 0.1318 1 6 40 0.0294 l 2 96 02105
Exa.mincSullil.imbllily ofFish~ l 5 ((l 0.1318 I 7 20 0.0147 I 6 20 0.0438 l~ioghup~ ofC!imato Chul&-e I 6 :w O.o659 I 2 YJO 0.1616 1 9 to 0.0219
M<W~~<Jril18 N&~ur&l A•enu..C<W~ of ~d1Joll 0~ I 9 to 0.0219 1 8 to 0.0(113 1 8 11 0.0241 f-' O'l I I ' LT1
l:lui¢ I::quipnltt•l f« J~id.W Oil Cleul Up I 100 0.!3t'9 I 7 10 0_0250 1 I 4 45 (10576
l:du:.tt:ion 81ld Trainill8 l 5 25 o.os.n t 1 160 0.4000 l 3 225 0.21!8-1
r:1v.b~sb Mrn~mng Sy:~~e~n I 3 4() 0,1355 I 5 20 0 .OSIJO I 2 225 02884
l.ogiSI~ InfonnMim Sh&rill8 I 4 4() o,l355 1 4 40 0,1000 1 I 225 02884
Slrt81l!lioe llt.XIY~' t'U!Id l'roeedure l 6 20 0.0677 1 6 10 o 0250 1 6 20 0_0256
fun~l• fc:r ln:rease~IC11mmuni!y M~ng .. •l'rog,..n.! l 7 10 o.ill38 I s so 02000 l 7 ](I 0.0128
_l"ari!h A<lmini:~tn.th"' C~ty I 2 ro 0,2033 I 2 80 02000 1 5 30 0,0384
I I t'U!Iding f« Go Yt1110lttd I I 90 0.3-161 1 I 20 0.1333 .I 5 ](I o.0085 1 011~• Progr~n!! "' l'r<ITIQ!o T Quri:.,nll'i!hori~ I 2 70 0.2692 I 3 10 0.1666 1 3 50 o.~1Z7 1
EcooonU; r\q~~&o:-ullurc Studle;J I 4 4() 0.1538 1 5 10 0.1666 1 I 1000 o 8547 1 Stipend for 11-u:iirlt:l! Adjlllb\1(111 tl New En~iro Rtg~Jluito 5 10 0.<138-1 I 4 10 0,1666 l 4 JO 0.0085 1 I.Qilll tc:r Sman ll11!inetl IJ..,..,l~menl I 3 50 0.1923 1 2 10 0.1666 1 2 IOO 0.085\ 1
I I I I Cooummity Oulrcacll Uld Supprnl'rogra.ms I 4 ((l 0.1395 l > so 0.0956 1 5 150 0.0038 I
l:lal,.ttl:du:ationl'rograrn.! l 5 40 o <1930 I l 500 05980 1 6 15 0 0019 1 l'ul•li~> lb11h Slall'i:ns &lid Swv<:OIW.~ IAIA\>1.1~ I 2 90 0.2093 I ~ ·10 0.047l! 1 ·I ISOO 0.0382 1
s,~ietll Ioqro''C ~icily f(ll' Pllbl~ lbllb Programs I 1 100 02325 1 2 160 0.1913 1 2 15000 031121 1
ID:.~c MCJJlallbllb C~Hy I 3 ";X) OJ627 I 7 11 0.0131 l 3 7500 0.1910 l l'opul&tioo an:! DetUog~ic Studies I g j(J 0.0232 1 6 15 o.ol19 1 7 I.S o.0003 1 l-&li~~Tran!h1i;m I 7 20 0.0465 t 8 10 o,OI19 1 8 10 o 0002 1
Ioqro''C Med~ Sm-i;¢:1 I 6 4() o 09J{J 1 5 20 o,0239 1 l 15000 o 3821 1
RAW DATA
COMMUNITY 0/t.SED REI'RE..'li;N(A.TIWS l'Xl.'ERT OROVI' Lijl~rtl3 I LLJ>!!rll4 1 up.ertl5
OlojutM~ I ]tam; Raw Wcl.!;ld I lw..l Raw Wd!,'l•l I ]tan\: Raw Wdgbt
Envir~ID1lentU I I 360 0.6792 1 1 .~o 0.~00 1 2 90 (1.1956
L('Sillical Cqndly liu J)i,..,$1:0' ll~JX)me I 4 10 0.0188 1 4 10 0.12.50 1 I 270 05869
Rcgilml E'-OOcmio Slab~ity I 2 12(1 (1.22(4 I 2 20 0.2500 l 3 90 01956
SocietLiln~~ I 3 40 Ct07S.t I J 10 CU2.51J I 4 10 (1.0211
I I Rer•w''&l and UiJpo~ of J\elidUll Oil I 3 12(10 0.0932 1 ;l 1::xl 0.14ll 1 1 1&:100 0527?
{'yJ1=1 Swoan11 ~~~ti<.II I I 7200 O.SSS\1 I j 360 OA23S 1 2 600() 0.1759
C11n.<lru:.li:ln Qf()y~ll:r !lee!~ I 8 20 0,0015 1 4 lXI 0,1411 1 4 3000 0 0879
Enviroomcmal River DiYt:111i1ru I 2 3((10 a.279? I 2 1::xl 0.1411 1 5 1.5(10 (I (l.f39
R~t<ntiou of &triet l:!laro~ I 1 40 0.0031 1 6 40 o.ono 1 6 1.51JO 0.0439
Uerbivcry l're\...,ti\WI I 9 10 o.OOQ1 I 9 10 0.0117 1 9 10 0.0002
E~~neSu1t1.inabillty ()J'Fi~·~ I 6 so 0,0062 1 5 40 0.0470 1 3 3000 0 0879
Re~Jl8 Impac.tl mC!ima.1eO.a.nge I 5 320 0.0248 l 7 20 0.0235 1 ? 1000 oo:m 1 1-' O'l Monitc.ing N&!utal Aletllllilian of R~idUll Oil I 4 .wo O.Cr310 I 8 2(1 O.Cr235 .I 8 100 0.0029 1 O'l
l I l I &1~ EquiJm~l for Re;~idml 0~ Clea.n Up l 4 90 0.0466 1 5 10 0.0222 l I 3ffl0 o.76B 1 Education and Training I 3 135 0.0699 I 2 12(1 0.26ift 1 2 ?20 0.1528 I
.E:IIabliJh MooitcrirJ& Sy:lletn I 6 30 O.OJ.5S I .. 20 0.04·1-1 1 s 4() o.oos.1 1 l.()gi.<lnl lnlalt\'llion Slu.rif18 I 1 121.5 0.6295 1 1 2·1() o.s:;r, 1 3 2.0 0.0.509 1
Stre4Dilioe Recovery Fund Pro(.C(jjue I 2 405 O.XI98 I 3 40 0.0888 l 4 80 00169 1
Funds fa ln~ C.ommunity:Med:iogs1Program.1 I 1 10 O.OJ51 l 1 1(1 0.0222 1 1 10 0.0021 I
l'ui:4! Adutini:llr&1iye Capacity I 5 45 0.02n 1 6 10 0.0222 1 6 20 o.o~12 1 I i I
Funding fa Gcm:mm~1 I 3 60 0.0937 1 3 60 0.0937 l 2 .500 0,1845 1
Outreac.h Programs to Pr001ole Tolllismil'isbe:rie;~ I 1 J(o(l 0.5625 I 2 1&::1 0.2812 1 3 ISO (10553 1
l:i.!ofJOfJLic AqUiCUbute Stu:lie-J I ·1 30 0.~~68 1 5 10 O.OB6 I I 2000 0.73110
S1ipeod ll:lr fl.u1in~ ;.<lill11rnen11~ :New Envir<J Regub.1~m s 10 0.0156 1 ·I 30 0.~168 l 5 10 0.0036
Loem fa Small BruinC;I;!I DevcloJm~t I 2 180 0.2812 1 I Jro 0 . .5625 l 4 50 00184
I I I Comrnuoity Q,fl'e!dl and Suwm l'tograru~ I 6 60 o.02S6 1 s 10 O.OISJ I 8 10 0.0016
!1(11Her r".d..,.,1iJnl'msn:m• I 7 30 0.0128 1 3 80 0.1212 1 6 20 00032
Public lb.Jfl Slilffill8 and SurYaJJao;e Dat&bi;Je I 1 I ZOO os139 1 5 20 0.0303 1 3 .500 ooros Sooetil lmptove Capacity fa !'ubi~ lb.ldJ l'togram~ I 2 ((JO 0.25(8 I 2 1((1 0.2424 t 2 2500 04(129
ln=e M~al Htaltb C~l)' I ,f 160 o.06~ I ·I .fO tt0606 1 4 120 0.0193
l'opul•ti oo an:! J):m(lgra.Jlh>. Stu:lle! I 5 ?5 0,0321 1 6 20 0.0303 1 7 15 00024
L a.ng t1 S@C: T ra.ru la1 i1 o I 8 10 0.0042 1 ? 10 o,OIS! 1 5 40 00064
JruproveMedi.:al Sen'~ 1 3 200 0.0856 1 I 32(1 0.4tt4tt I 1 3000 0.4834
RAW DATA
COMMUNJT Y n,o,.;sr:D Rl; !'RP.; f;Nf A Tl.Vf.S EX !'ERT (lROV I' Ullotril6 I £ipotrl17 I :U1~otrllll I
Objtdlves l lWil luw Wei,ght I Jw& Raw Wd81tC! lWil luw Wd,gld I
F:nvirmm"""&l I I 100 o.?6112 I 2 .,o 0.19:1-t I I 60 o .. \615 1
Logistal C&pQCily for Disa:st:r Re!prnsc l 3 10 0.0769 I I 120 05714 1 4 10 o 0769 1 Rcgirna.l Ecrncmic Sc&bilily I 2 10 O.O?W I 3 40 0.19:14 I 3 30 0.2Wl 1 Sociml bnpact~ I 4 10 o.0769 1 ~ 10 O.O.t76 I 2 30 0.2307 1
I l I R.em<TY&l Uld J)j"''(ll&l ofl!e1xhl!-1 011 I 7 ::xl O.o\00 1 1 720 O.H.Sl! t 8 10 0.0103 1
Cypre33 Swamp Res~raeirn l 2 ro o 1600 1 5 90 0.0557 1 I 320 0.329!1 1
Cortslru:Hon ofOyJiet Rt"Cfi I I 160 OJ2W I 6 90 0.0557 1 2 320 0.3298 1 Enviroofllttda.l RiYtt Di\tt!lionl I 6 "" 0.0800 1 3 ISO 0.1 ll.t 1 3 160 0.16491
Re!I<F&ll_1n <:tfll.uric:r 1~.,.;1~ I 4 60 o 1200 1 2 :360 02229 1 5 40 o 0412 1
lbbivay l'revrnlioo I 9 10 0.0200 I 4 90 0.0557 1 9 10 00103 I E.u.nW!e Swl!i~bijity ofFilllffiel I 3 XI 0.1400 1 8 30 o 0185 1 4 80 o .0824 t R-.vcltins lmJ"'c"' <:tf Clim~11:: C'hUtse I 5 ·\5 0.0900 1 9 10 0.0051 1 6 20 0.0206
t-> O"l :Mrnnro113 Nl.lwal Aac:on&tirn of Residual 0~ I 8 15 o.moo 1 7 45 o.o2111 1 7 10 0.0103 -...J
I I I twlc Equlpmettt fa- Rt:3idu11 Oil Clwt Up l 2 6() 0.19~' 1 I ·iBO M32.t 1 6 20 0.0307
Edi>04ti.,nUtd r .... ;nj,l!! I 4 :J>:l oo952 1 2 240 0.2162 i 3 ISO 0.2769
B!l&bMt Mrnnmna S)•stem I 7 10 oron 1 3 240 02162 1 2 180 02769
l.ogi:itkal Jofomutioo Shlrifi8 I 6 15 0.0476 1 4 80 0.0720 1 1 180 027(f)
Stre.vnlin~ Rowvery l'!IDd l'ro=4t~ l 3 YJ 0.1587 1 5 ·'0 0.01-50 1 ~ 60 0.0923
l'un1ll f(ll' ln:;r~,edC<:tmmlat;,y Mo;'lirt,!!~''l'rog~, I 5 :J>:l o 0952 1 6 20 00180 1 7 10 00153
Pailb Adnrinistraei~ C&pl.cily I 1 12(1 0.3809 l 1 10 0.0090 1 5 20 0 (607
I I l'lllldirt,!! f(lr {'.<Jvo:rnnt""" I 5 10 0.0800 l 5 10 0.(JI.~9 ,, 100 0.0862
Ouv-eaclt Programa to Pr001ot:; TQurisn/Fiibcries I 4 10 o.osoo 1 3 40 0.0597 2 :J>JO 02586
l::c<tooo~ AqUi¢Uilute Studit:l I 3 15 01200 1 4 20 0.0298 3 ]50 0.1293
Sripc1ld for I:I'U:Iioe:!s Aqjwtu~ll:t ~ £r1YitO R~'llhlitrt I 6() O .. tBOO I 2 120 0.17111 5 )() o.oo86 1 l.(l&nl f(lr Small 11-!t.linel.'l D~l~mCI!ll I '2 :J>:l 02400 1 l 4110 0.7164 I 600 05172 1
I I Coo1.11111rthy Outrt-aclt a.nd SuwM l'rogra.nu I 3 2(1 00833 1 1 320 0.4507 ? 10 0.0192 1 ll<:tl~c:r &hr~xm l'rQsnt.n'" I 2 "" 0.1666 1 6 30 O.O•t22 6 20 0.0381 I
Public l:k&lft Sta.ffing a.nd Survc~la.nc.c Dll.t&bo!c l 5 15 o 0625 1 4 60 00815 2 90 o 17:J>J 1
So;.iC'Ial lmp'Q~ Capacity fCl' l'llblic l:k&ldl Progr&m.3 I 1 12(1 05000 l 2 160 0.2253 3 90 01?301 !n..~CaJe Metda.l lb.! Ill C;tpacily I
,, 15 0.0625 1 3 so 0.1126 1 1110 03\61 1 l'qt\ll&'lim ..,.,i D<m<JBilo}il); Sin.i~ I 6 10 0.0416 1 5 30 O.O-t22 " 60 0.1153 l
La:nguagc Tnruhlion I 7 10 00416 1 7 20 002111 g 10 o 0192 1
lrtlJWO\'e Meodkal SttV~ I 8 10 O.OH6 I 8 10 0.0140 5 60 OJ153 I
RAW DATA
COMMUNI! Y BASED REl'RB'!ENf A TIVFS EXPERT GROUP
r~rt21l 1 F.>: pert Jl()
Objtdh't\! I R-""k R...., w•1 R4llk R•w w• £tJ viromuenlal I 1 60 0 . .5217 1 I 260 o .. IJ26 1 I..<J!iSiiaLI C.q~acity fof Di!.,.,lo::f R¢'111Qil'l~ I 4 10 0,01164 1 4 10 O.CI277
RC(iooal.&x«<~ Slabilily I 2 30 o.zros 1 3 100 O.L~7
SacietallntJ m:ti l 3 IS o.l30·1 1 2 260 0..1126 l I
RmDV&I and DnpoS&l ofRC3idual Oil I 9 10 0.0118 1 ~ 13(1 0.0557
Cypr~• Swa>q1 llO!lor!.IJo21 I 2 Z70 0.319S l 2 511S 0,2507
Cm~tnlcti<m of Oy Sle<r R¢~ I 6 4S 0.0532 1 s 100 0008
Fnv iroom(l)tal R.iv<r DiYersioru I 1 270 o.Jl95 1 3 390 0.1671
Reslol'lllim of Burio. blm:b I 4 90 o.I06S 1 1 511S 0.2507
Hativ<W)' Prc>i(l)lion I 8 10 0.0118 1 9 10 O.o::»2 .Ewuioo &Jst.C.a.bility of l'isbc•:itr:. 1 3 90 o.I06S 1 7 100 0.0128
R~ins l"l'.a~ ofClim•e C'il"'lg. I 5 45 o.0532 1 5 173 0.0741
Mooi1olins Nalur&l r\lt(l)u&liou ofRC3idu&l Oil t 7 15 0.0177 I 4 ~0 0.1114 I-> en 00 14:•ic r:<Juipm(lll f(lr R"'xh~l Oil ne... Up l 7 10 o.0169 1 5 13 0.0866
Edoca.tioo &n.i Traioins l 3 so o.ms 1 ~ 10 0.~6
Eatabli:sb Mwitociug Sy ,. .. " I 2 160 O.Z711 1 4 13 0.0866
I"'Si•t>:...J JnfQrmlli,, ~.;rins I I 200 On89 I 3 26 0,1733
Slreamlioo Re-;:QV<ry Fund Pro:cdurc I 4 so 0.1355 1 1 52 0.3-l{;(i
Funds for l11aeoued CCIIIllllunity M«<iu.!l''l't~ll I 6 20 o.ons 1 2 26 0.1733
l'ari!h Adminillr&4ivc C~aci1y I 5 40 O.()(i77 I 7 10 0.(1666
I I Fun.iin.!; for GO\'elll'"'"" I 4 30 0.0447 1 3 130 0,1710
Oulre&d! Prowa.nu lo Prom<>!e T oorum1Fn~ I 3 90 0.134l 1 4 100 0.1315
fumucic Aqua:.Jlture St1di01 I 2 ISO 0.2686 1 2 260 0.3421
Slipen.i f<l' lln~-• Nljl"tm(lll 111 Now F.11vir<l Re,s1h1i 1 s 10 0,0149 1 s 10 0.0131
l.oa:JS fa- Small BwinC!IS Dtv¢llll mllll I 1 ~0 o.sm 1 1 260 0.3421
I Ccwrun•mity Ou!reocll an:l S•'I'JIQrt Pro !rom• I s 40 0.0470 1 4 ISO 01063
Bolstc:r Edu;;Uloo Prowa.ma I 1 40 0.\WO I 6 10<1 0.(17(J9
J'ublieH....!tl• SW11n,!l and Sun'l>ills•ce uuabue I 2 210 0.2823 1 s ISO 0.1063
Societal lmprov¢ c~ a.'ily f<~ Public Heallb PrOI,Ifaml I 1 240 0.2823 1 7 100 O.(J7(J9
~~~ Mrual Healt.l• C;~.padty I 3 120 o.MII 1 2 3SO O.US2
.PQJilll•xm ""d DemQST~ ID; St.-iiaJ I 6 40 o.047o 1 :l ;2()0 0,1418
l.aoJJuSI.ll' Tn.mhli<m I s 10 0.0117 1 8 10 0.0070
luLprove M ttld Servicn; l ,, 120 0.1411 I 1 3SO 0.2a!2
References
Adams, C., Hernandez, E., Cato, J., The economic significance of the Gulf of Mexico related to population, income, employment, minerals, fisheries, and shipping. Ocean & Coastal Management 47 (2004) 565-580
Aguilera, F., Mendez, J., Pasaro, E., Laffon, B. (2010) Review ofhealth effects to spilled oils on human health. J. applied toxicology 30:291-301.
Alemi, F., Gustafson, D. (2007). Decision Analysis for Healthcare Managers. Health Administration Press. Chapters 1, 2, 3, 5, 6, 10, 11.
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